Software Engineer, Research Acceleration at thinkingmachines | San Francisco

At Thinking Machines Lab, our mission is to empower humanity by advancing collaborative general intelligence. We aspire to create a future where everyone can access the knowledge and tools necessary to harness AI for their individual needs and aspirations.Our team consists of scientists, engineers, and innovators who have developed some of the most renowned AI products, including ChatGPT and Character.ai, as well as open-weight models such as Mistral. We are also contributors to popular open-source initiatives like PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are seeking talented engineers to develop the libraries and tools that will expedite research at Thinking Machines. You will take charge of our internal infrastructure, which includes evaluation libraries, reinforcement learning training libraries, and experiment tracking platforms, all aimed at enhancing research velocity over time.This position emphasizes collaboration; you will engage directly with researchers to pinpoint bottlenecks and challenges. Your success will be measured by the trust researchers place in your systems and their enjoyment of using them.What You'll DoDesign, develop, and manage research infrastructure, including evaluation frameworks, RL training systems, experiment tracking platforms, visualization tools, and shared utilities.Create high-throughput, scalable pipelines for distributed evaluation, reward modeling, and multimodal assessments.Establish systems for reproducibility, traceability, and stringent quality control throughout research experiments and model training processes. Implement monitoring and observability.Collaborate closely with researchers to identify obstacles and unlock new capabilities. Manage research tools like a product manager, actively seeking feedback and tracking user adoption.Work alongside infrastructure, data, and product teams to ensure seamless integration of tools across the technical stack.

At Thinking Machines Lab, we are committed to empowering humanity by advancing collaborative general intelligence. Our vision is to create a future where everyone has access to the knowledge and tools necessary to harness AI for their unique needs and aspirations.Our team comprises scientists, engineers, and builders who have developed some of the most utilized AI products, including ChatGPT and Character.ai, as well as open-weight models like Mistral. We also contribute to notable open-source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are seeking a talented Infrastructure Research Engineer to enhance, scale, and fortify the systems supporting Tinker. This role will enable our internal teams and external clients to fine-tune models seamlessly, reliably, and cost-effectively. You will work at the intersection of large-scale training systems and product infrastructure, creating multi-tenant scheduling, storage, observability, and reliability features within a developer-friendly API.Your contributions will allow all Tinker users to concentrate on research and development without the burden of infrastructure concerns.Note: This is an evergreen position that we keep open for ongoing interest. We receive numerous applications, and there may not always be a role that aligns perfectly with your skills and experience. We encourage you to apply, as we continuously review applications and will reach out as new opportunities arise. You are welcome to reapply after gaining more experience, but please refrain from applying more than once every 6 months. We also post specific roles for unique project or team needs, and you are welcome to apply directly to those in addition to this evergreen listing.What You’ll DoDesign and implement distributed job orchestration, placement, preemption, and fair-share scheduling to enhance Tinker for multi-tenant workloads.Optimize GPU utilization, throughput, and reliability across clusters (including autoscaling, bin-packing, and quotas).Develop reusable frameworks and libraries to enhance Tinker’s transparency, reproducibility, and performance.Collaborate with researchers and developer experience engineers to transform fine-tuning challenges into product features.Publish and disseminate insights through internal documentation, open-source libraries, or technical reports to advance the field of scalable AI infrastructure.

At Thinking Machines Lab, our mission is to empower humanity by advancing collaborative general intelligence. We envision a future where everyone has access to the knowledge and tools necessary to tailor AI to their unique needs and aspirations.Our team consists of scientists, engineers, and innovators who have developed some of the most widely utilized AI products, such as ChatGPT and Character.ai. We are also the creators of open-weight models like Mistral, along with popular open-source projects including PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are in search of a talented Full Stack Engineer to create and deploy products from initial prototype to full-scale implementation. You will maintain tools that enhance the efficiency of our research and product teams, working on both frontend and backend components while contributing to the reliability, observability, and security of our production environment.This position is categorized as an

At Thinking Machines Lab, our vision is to enhance human potential by advancing collaborative general intelligence. We are dedicated to creating an inclusive future where everyone can harness AI's capabilities tailored to their unique aspirations.Our team comprises scientists, engineers, and innovators behind some of the most impactful AI solutions, including ChatGPT and Character.ai, as well as open-source projects like PyTorch and Segment Anything.About the RoleWe are seeking a talented Software Engineer to architect, develop, and maintain the GPU supercomputing infrastructure essential for large-scale AI training and inference. Your contributions will ensure high-performance, reliable, and cost-effective computing resources, enabling our users and researchers to achieve rapid advancements at scale.This is an "evergreen role," open for ongoing interest. We receive numerous applications, and while an immediate fit may not always be available, we encourage you to apply. We actively review applications and reach out when new opportunities arise. Reapplications are welcome after six months, and we also post specific roles for unique projects or teams.What You’ll DoAutomate and manage large GPU clusters, handling provisioning, imaging, and capacity strategy.Develop software that simplifies cluster management, providing a cohesive interface for training and inference tasks.Enhance scheduling and orchestration frameworks (Kubernetes, Slurm, or similar) for optimized resource allocation, preemption, and multi-tenancy management.Monitor and improve operational efficiency, focusing on speed, reliability, and error recovery mechanisms.Design robust storage solutions for datasets, checkpoints, and logs, ensuring clear data retention and lineage.Collaborate with researchers to facilitate large-scale experiments, offering guidance on parallelism and performance considerations.

At Thinking Machines Lab, our mission is to enhance human capabilities through the development of collaborative general intelligence. We are dedicated to creating a future where everyone can utilize AI tailored to their specific needs and aspirations.Our team consists of accomplished scientists, engineers, and innovators responsible for some of the most popular AI applications, including ChatGPT and Character.ai, along with renowned open-weight models like Mistral and influential open-source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are on the lookout for a passionate Software Engineer with a focus on security to ensure our products are secure by design while facilitating rapid and ambitious product development. You will collaborate closely with product and research teams to integrate security measures into the design and development processes, and create tools and automation to maintain system safety at scale.Note: This is an ongoing opportunity, and we encourage you to express your interest. While we receive numerous applications and there may not always be an immediate match for your skills, we encourage you to apply. We consistently review applications and will reach out as new roles become available. You may reapply if you gain additional experience, but please limit applications to once every six months. We also post specific roles for particular projects or teams, and you are welcome to apply for those as well.What You’ll DoCollaborate with product and research teams to integrate security into the development lifecycle: threat modeling, design reviews, and establishing secure defaults for new features.Design and implement security controls throughout our product stack (authentication, authorization, session management, input validation, etc.).Create and maintain security tooling and automation for engineers: secure frameworks and templates, CI/CD checks, dependency management, and vulnerability detection.Work alongside researchers to identify and address AI-specific product risks, such as model abuse, prompt injection, data leakage, or misuse of capabilities.Enhance observability and detection for security-related events: access anomalies, abuse patterns, and suspicious behavior in production.

Join Our Innovative TeamAt OpenAI, our Kernels team is at the forefront of developing cutting-edge software that drives our most ambitious AI research initiatives.We operate at the intersection of hardware and software, crafting high-performance kernels and implementing distributed system optimizations to enhance the efficiency of large-scale training and inference processes.Our mission is to empower OpenAI to push the boundaries of AI by ensuring that various models—from large language models (LLMs) to recommendation systems—operate seamlessly on state-of-the-art supercomputing infrastructures. This includes adapting our software stack for new accelerator technologies, optimizing overall system performance, and eliminating bottlenecks throughout the architecture.Your RoleAs a member of the Accelerators team, you will play a crucial role in evaluating and integrating new computing platforms designed to support extensive AI training and inference capabilities.Your projects will encompass everything from prototyping system software on emerging accelerators to implementing performance enhancements across our AI applications.You will engage with both hardware and software components, focusing on kernel development, sharding strategies, distributed systems scalability, and performance modeling.This position emphasizes the integration of machine learning algorithms with system performance optimization—particularly in large-scale environments—rather than solely compiler development.Key ResponsibilitiesPrototype and empower OpenAI's AI software stack on pioneering accelerator platforms.Enhance the performance of large-scale models (LLMs, recommender systems, distributed AI workloads) across varied hardware setups.Design kernels, sharding strategies, and system scaling solutions optimized for new accelerator technologies.Collaborate on code-level optimizations (e.g., in PyTorch) and lower-level enhancements to improve performance on unconventional hardware. Conduct system-level performance modeling, identify bottlenecks, and foster comprehensive optimization.Partner with hardware teams and vendors to assess alternatives to current platforms and adapt our software stack accordingly.Contribute to runtime advancements, compute and communication overlapping, and scaling strategies for next-generation AI workloads.Ideal Candidate ProfileA strong background in software engineering, particularly with a focus on system performance and large-scale applications.Experience with AI workloads and optimizing performance across both hardware and software layers.Familiarity with distributed systems and the ability to work collaboratively with hardware teams.A passion for advancing AI technologies and a desire to tackle challenging problems in a fast-paced environment.

At Thinking Machines Lab, our ambition is to enhance human potential by advancing collaborative general intelligence. We envision a future where individuals have the tools and knowledge to harness AI for their distinct requirements and aspirations.Our team comprises dedicated scientists, engineers, and innovators who have contributed to some of the most renowned AI products, including ChatGPT and Character.ai, along with open-weight models like Mistral, and influential open-source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are seeking an Infrastructure Research Engineer to architect, optimize, and sustain the computational frameworks that facilitate large-scale language model training. You will create high-performance machine learning kernels (e.g., CUDA, CuTe, Triton), enable effective low-precision arithmetic operations, and enhance the distributed computing infrastructure essential for training expansive models.This position is ideal for an engineer who thrives in close collaboration with hardware and research disciplines. You will partner with researchers and systems architects to merge algorithmic design with hardware efficiency. Your responsibilities will include prototyping new kernel implementations, evaluating performance across various hardware generations, and helping to establish the numerical and parallelism strategies crucial for scaling next-generation AI systems.Note: This is an evergreen role that remains open continuously for expressions of interest. We receive numerous applications, and there may not always be an immediate opportunity that aligns with your qualifications. However, we encourage you to apply, as we regularly assess applications and will reach out as new positions become available. You are also welcome to reapply after gaining additional experience, but please refrain from applying more than once every six months. Additionally, you may notice postings for specific roles catering to particular projects or team needs. In such cases, you are encouraged to apply directly alongside this evergreen listing.What You’ll DoDesign and develop custom ML kernels (e.g., CUDA, CuTe, Triton) for key LLM operations such as attention, matrix multiplication, gating, and normalization, optimized for contemporary GPU and accelerator architectures.Conceptualize compute primitives aimed at alleviating memory bandwidth bottlenecks and enhancing kernel compute efficiency.Collaborate with research teams to synchronize kernel-level optimizations with model architecture and algorithmic objectives.Create and maintain a library of reusable kernels and performance benchmarks that serve as the foundation for internal model training.Contribute to the stability and scalability of our infrastructure, ensuring it meets the growing demands of AI development.

Join Perplexity as a Software Engineer on our innovative Acceleration team! We are at the forefront of transforming how individuals navigate the internet and the world around them. Our goal is to enhance the operational efficiency of our teams and achieve significant advancements in both product and user experience.As we lead the way into an agentic future for the internet, you will play a crucial role in leveraging AI tools and agents to amplify the capabilities of our focused, mission-driven teams. Currently, AI is integrated into every aspect of our work, from frontend and backend engineering to applied AI research and business operations. The Acceleration team is dedicated to pioneering software and process engineering to maximize the benefits of AI technology, ensuring we deliver at unparalleled speeds.We are looking for candidates with exceptional technical judgment developed through experience in internet-scale companies, who actively engage with cutting-edge AI tools and possess a visionary mindset to redefine work processes for future-leading organizations. You will thrive in a versatile role that combines AI product development, infrastructure/platform engineering, developer experience, and more.

Thinking Machines Lab aims to advance collaborative general intelligence, making AI accessible and adaptable for individuals and organizations. The team brings together scientists, engineers, and innovators behind well-known AI solutions, including ChatGPT, Character.ai, Mistral, and open-source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything. Tinker, the lab’s fine-tuning API, helps researchers and developers customize AI models using their own data and algorithms. By handling the infrastructure, Tinker allows users to focus on training and deploying models that suit their needs. With a growing customer base and expanding features, the team is looking for a Software Engineer, Platform to support Tinker's continued development. Role overview This position centers on building and maintaining the core platform systems that power Tinker. The engineer will manage billing and usage metering, permissions and access control, organizational structures, data exports, audit logging, and the administrative tools that tie these systems together. Collaboration with product and legal teams is essential, as changes to features, pricing, and enterprise agreements will involve this role. What you will do Design the authorization layer for all products, including RBAC, API key scoping, organizational hierarchies, and permission boundaries. Oversee billing infrastructure, covering usage metering, plan management, payment processing, invoicing, and revenue recognition support. Develop and improve models for organizations and teams, such as seat management, SSO/SAML, workspace isolation, and invitation flows. Implement data export and deletion processes that align with enterprise standards and data residency requirements. Create audit logging systems to track user actions and decisions. This role is based in San Francisco.

At Thinking Machines Lab, we strive to empower humanity by advancing collaborative general intelligence. Our vision is to create a future where everyone can access the knowledge and tools necessary to harness AI for their specific needs and aspirations.Our team comprises scientists, engineers, and innovators who have developed some of the most widely utilized AI products, such as ChatGPT and Character.ai, along with notable open-weight models like Mistral, as well as prominent open-source projects including PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleAs a Research Product Manager (RPM) at Thinking Machines Lab, you will play a pivotal role in driving complex, high-impact technical products and programs that encompass research, infrastructure, and applied initiatives. You will facilitate the transformation of ambitious concepts into reality by propelling cross-functional collaboration, ensuring projects maintain momentum, and fostering clarity in fast-paced, ambiguous settings.Your contributions will connect people, ideas, and systems, guaranteeing that our critical research initiatives remain aligned, well-defined, and progressing efficiently. This position is ideal for someone who excels in technical discussions, comprehends the intricacies of research, can conceptualize at a high level while also delving into detailed aspects, ultimately aiming to assist the company in executing at scale.Note: This is an "evergreen role" that we keep open on an ongoing basis to express interest. We receive numerous applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Nevertheless, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities arise. You are welcome to reapply if you gain more experience, but please refrain from applying more than once every six months. You may also find that we post job openings for specific roles related to separate projects or team needs. In those cases, you are welcome to apply directly in addition to this evergreen role.What You’ll DoDrive and coordinate large-scale research products and programs, ensuring that complex projects are executed efficiently, transparently, and with scientific rigor.Translate technical ideas into actionable, well-scoped plans, defining milestones and ensuring team alignment across model development, data campaigns, infrastructure, and product integration.Collaborate across disciplines—from research and ML infrastructure to legal and business development—quickly ramping up on new domains as necessary.Create and maintain compute and resource roadmaps, identifying bottlenecks and solutions to optimize project flow.

Thinking Machines Lab brings together scientists, engineers, and innovators who have contributed to well-known AI products such as ChatGPT, Character.ai, and open-weight models like Mistral. The team’s open-source projects include PyTorch, OpenAI Gym, Fairseq, and Segment Anything. Their mission centers on advancing collaborative general intelligence and making AI tools accessible for a wide range of users and goals. The Tinker platform offers a fine-tuning API that lets researchers and developers tailor advanced AI models to their needs. By handling the underlying infrastructure, Tinker enables users to train open-weight models with custom data, algorithms, and objectives. As demand grows, the team is adding new features and supporting an expanding community. Role overview The Full Stack Software Engineer will play a key part in building and maintaining the products and services that Tinker users depend on. This position involves working closely with frontend, backend, and infrastructure teams to deliver the Tinker console, developer tools, and essential features. What you will do Develop and enhance Tinker’s APIs and backend services using Python and Rust, focusing on areas like job submission, orchestration, billing, and usage tracking. Design and launch user interfaces, including the Tinker console and upcoming developer tools, using React and TypeScript. Refine the developer experience by improving SDK usability, error messages, API design, and onboarding processes. Work to increase system reliability, observability, and security in production, and participate in on-call rotations. Create internal tools that help research and infrastructure teams work more efficiently. Location This role is based in San Francisco, California.

About CartesiaAt Cartesia, our vision is to develop the next wave of artificial intelligence: a seamless, interactive intelligence that is accessible anytime and anywhere. Even the most advanced models today struggle to consistently analyze extensive streams of audio, video, and text—this includes a staggering 1 billion text tokens, 10 billion audio tokens, and 1 trillion video tokens—much less accomplishing this directly on devices.We are at the forefront of designing the model architectures that will revolutionize this capability. Our founding team, who met as PhD students at the Stanford AI Lab, pioneered State Space Models (SSMs), a groundbreaking tool for training efficient, large-scale foundational models. Our diverse team blends in-depth knowledge of model innovation with strong systems engineering and a product-driven engineering approach to create and deploy cutting-edge models and experiences.We are backed by prestigious investors including Index Ventures and Lightspeed Venture Partners, along with contributions from Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks, and many others. We are privileged to have the mentorship of numerous esteemed advisors and over 90 angel investors from various fields, including leading experts in AI.About the RoleWe are seeking an AI-native Software Engineer focused on Developer Acceleration to enhance the developer experience and optimize the speed at which Cartesia engineers can ship solutions. This role involves creating innovative tooling at the cutting edge of AI programming, and developing a comprehensive playbook that empowers both engineers and non-engineers to efficiently deploy consistent and maintainable internal tools independently.Your ImpactDesign workflows that facilitate Cartesia's transition from problem identification to solution implementation with minimal human oversight.Remain informed about the latest advancements in AI-assisted development and champion its adoption within Cartesia.Develop automated end-to-end development and evaluation frameworks that empower coding agents to refine solutions and self-correct.Create a playbook for the Cartesia team to build data-connected, IAM-aware internal tools for both human-in-the-loop and automated processes.

At Thinking Machines Lab, our mission is to empower humanity by advancing collaborative general intelligence. We are dedicated to building a future where everyone can access the knowledge and tools necessary to harness AI for their unique needs and objectives.We are a team of scientists, engineers, and builders who have developed some of the most widely used AI products, including ChatGPT and Character.ai, and contributed to open-weight models like Mistral, along with popular open-source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are seeking an Infrastructure Engineer to take charge of evolving the security infrastructure that supports our foundational models. In this pivotal role, you will collaborate across computing, storage, networking, and data platforms to ensure our systems remain secure, reliable, and scalable. You will design controls, architecture, and tooling that embed security into the platform's core functionalities. Working closely with research and product teams, you will enable them to operate swiftly while safeguarding our models, data, and environments.Note: This is an "evergreen role" that we maintain for ongoing interest. While we receive numerous applications, there may not always be an immediate position that perfectly matches your skills and experience. We encourage you to apply, as we continuously assess applications and reach out to candidates when new opportunities arise. Feel free to reapply if you gain more experience, but please refrain from applying more than once every six months. Additionally, we occasionally post openings for specific roles to meet project or team-specific needs, and in those cases, you are welcome to apply directly in conjunction with this evergreen role.What You’ll DoDesign security patterns for platforms and services, including network segmentation, service-to-service authentication, RBAC, and policy enforcement in Kubernetes and cloud environments.Oversee identity, access, and secrets management for users and services: workload and cross-cloud identity, least-privilege IAM, and secrets management.Create secure platforms for data ingestion, processing, and curation, encompassing classification, encryption, access controls, and safe sharing practices across teams.Develop threat models and review designs with researchers and engineers to facilitate safe and scalable feature launches.Automate security checks and implement guardrails: policy-as-code, secure infrastructure baselines, CI/CD validation, and tools that streamline secure operations.

About the Role OpenAI is hiring a Software Engineer for the Engineering Acceleration team, working on Consumer Devices in San Francisco. This team builds and improves products that shape how people use technology in daily life. The role involves developing new features and strengthening existing systems for consumer-facing devices.

Thinking Machines Lab brings together scientists, engineers, and innovators who have contributed to well-known AI products such as ChatGPT, Character.ai, and open-source frameworks like PyTorch, OpenAI Gym, Fairseq, and Segment Anything. The team's mission centers on advancing collaborative general intelligence, aiming to make AI accessible for people to address their own needs and ambitions. The Tinker platform offers a fine-tuning API that lets researchers and developers tailor advanced AI models to their specific requirements. Tinker provides the infrastructure, while users maintain flexibility to train open-weight models with their own data and algorithms. As Tinker grows its features and user base, the team is expanding to support the platform's evolution. Role overview This Full Stack Software Engineer role focuses on designing, building, and maintaining the products and services that Tinker users rely on. The work covers frontend, backend, and infrastructure, with an emphasis on the Tinker console, developer tools, and meeting the changing needs of the Tinker community. What you will do Develop and improve Tinker’s APIs and backend services using Python and Rust, including systems for job submission, orchestration, billing, and usage tracking. Build user-facing interfaces such as the Tinker console and future developer tools with React and TypeScript. Enhance the developer experience by refining SDK usability, error messages, API design, and onboarding workflows. Increase system reliability, observability, and security in Tinker’s production environment, and participate in on-call rotations. Create internal tools to support the research and infrastructure teams working on Tinker. This position is based in San Francisco.

At Thinking Machines Lab, we are on a mission to empower humanity by advancing collaborative general intelligence. Our vision is to create a future where everyone has access to the knowledge and tools necessary to harness AI for their unique needs and objectives.We are a diverse team of scientists, engineers, and builders responsible for developing some of the most influential AI products on the market, such as ChatGPT and Character.ai. Our contributions extend to open-weight models like Mistral and popular open-source projects including PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are seeking talented engineers to join our team and develop the libraries and tools that will accelerate research efforts at Thinking Machines. You will take charge of our internal infrastructure—creating evaluation libraries, reinforcement learning training libraries, and experiment tracking platforms—while building systems that enhance research velocity over time.This position emphasizes collaboration. You will work closely with researchers to identify bottlenecks and pain points, ensuring that they trust your systems to function seamlessly and find them enjoyable to use.What You'll DoDesign, build, and manage research infrastructure, including evaluation frameworks, RL training systems, experiment tracking platforms, visualization tools, and shared utilities.Develop high-throughput, scalable pipelines for distributed evaluation, reward modeling, and multimodal assessment.Establish systems for reproducibility, traceability, and robust quality control across research experiments and model training runs, implementing effective monitoring and observability.Collaborate directly with researchers to identify bottlenecks and unlock new capabilities, managing research tools like a product manager by proactively seeking feedback and tracking adoption.Work alongside infrastructure, data, and product teams to integrate tools across the technical stack.

About MercorMercor sits at the forefront of labor markets and artificial intelligence research, collaborating with premier AI laboratories and enterprises to harness the human intelligence crucial for AI evolution.Our expansive talent network empowers the training of cutting-edge AI models, akin to how educators impart knowledge to students—sharing insights, experiences, and contexts that transcend mere code. Currently, our network comprises over 30,000 experts, generating collective earnings exceeding $2 million daily.At Mercor, we are pioneering a unique category of work where expertise fuels AI progress. Realizing this vision necessitates a bold, fast-paced, and deeply dedicated team. You will collaborate with researchers, operators, and AI firms that are at the vanguard of transforming systems that redefine society.As a profitable Series C company, Mercor is valued at $10 billion and maintains an in-office presence five days a week at our new headquarters in San Francisco.About the RoleIn your capacity as a Research Engineer at Mercor, you will operate at the intersection of engineering and applied AI research. You will play a pivotal role in post-training and Reinforcement Learning from Human Feedback (RLVR), synthetic data generation, and large-scale evaluation workflows essential for advancing frontier language models.Your contributions will help train large language models to adeptly utilize tools, exhibit agentic behavior, and engage in real-world reasoning within production environments. You will be instrumental in shaping rewards, conducting post-training experiments, and constructing scalable systems to enhance model performance. Your responsibilities will also include designing and evaluating datasets, creating scalable data augmentation pipelines, and developing rubrics and evaluators that expand the learning potential of LLMs.

Anara is seeking a talented Software Engineer to join our innovative team in revolutionizing scientific research. In this role, you will develop groundbreaking tools that will redefine the landscape of scientific discovery for generations.Your Responsibilities:Design and implement scalable features and infrastructure for cutting-edge AI-driven research tools.Collaborate directly with customers to gather valuable insights and enhance user experience.Continuously monitor and optimize system performance and costs, making strategic trade-offs to maintain agility.Establish best practices for prompt engineering and deployment of new models and workflows.Lead the design and automation of evaluation pipelines to consistently assess, benchmark, and enhance AI agent quality and reliability.

HUD builds infrastructure for generating and evaluating reinforcement learning (RL) training data for advanced AI agents. The team is also developing a marketplace to connect leading labs with high-quality training data. HUD's platform serves frontier labs, Fortune 500 companies, and startups. The company is backed by $15M in funding from top venture capital firms and is part of Y Combinator's W25 cohort. Role overview HUD is seeking Research Engineers in San Francisco to help strengthen quality assurance for training data produced by partner organizations. This position centers on building systems that maintain and improve data quality as demand increases. What you will do Set and uphold quality standards for training datasets. Develop tools and workflows for auditing datasets from suppliers, including sampling methods, validation pipelines (using rules and models), and feedback systems. Assess and refine human-in-the-loop review processes to support quality assurance. Collaborate with data vendors to resolve quality issues, share insights, and encourage better data generation practices. Integrate QA findings into internal tools and the data vendor portal to reduce anomalies, inconsistencies, and edge cases. Requirements Strong skills in Python, Docker, and Linux environments. Experience working with large datasets. Ability to learn quickly and adapt in technical contexts, such as programming competitions. Background in early-stage tech startups and ability to work independently. Familiarity with modern AI tools and large language models (LLMs). Clear communication skills for collaborating remotely across time zones. Preferred qualifications Understanding of common issues in training data. Background in building data validation pipelines or human-in-the-loop review systems. Strong attention to detail, with the ability to identify subtle data inconsistencies or edge cases. Experience designing metrics, experiments, and QA processes, not just executing them.

At Thinking Machines Lab, we are dedicated to empowering humanity by advancing collaborative general intelligence. Our vision is to create a future where everyone can leverage AI to meet their unique needs and aspirations.Our talented team comprises scientists, engineers, and innovators who have developed some of the most widely recognized AI products, including ChatGPT and Character.ai, alongside open-weight models like Mistral and popular open-source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the PositionWe are seeking a motivated Infrastructure Research Engineer to design, enhance, and scale the systems that underpin large AI models. Your contributions will significantly improve inference speed, cost-effectiveness, reliability, and reproducibility, allowing our teams to concentrate on enhancing model capabilities rather than dealing with bottlenecks.Our mission centers on delivering high-performance and efficient model inference to support real-world applications and accelerate research efforts. In this role, you will be responsible for the infrastructure that guarantees smooth operation for every experiment, evaluation, and deployment at scale.Note: This is an evergreen role, kept open continuously to express interest. We receive numerous applications and may not always have an immediate opening that aligns perfectly with your skills and experience. However, we encourage you to apply. We regularly review applications and reach out to candidates as new opportunities arise. Feel free to reapply as you gain more experience, but we kindly ask that you avoid applying more than once every six months. You may also notice postings for specific roles related to particular projects or teams, in which case you are welcome to apply directly in addition to this evergreen role.What You Will DoCollaborate with researchers and engineers to transition cutting-edge AI models into production.Partner with research teams to ensure high-performance inference for innovative architectures.Design and implement new techniques, tools, and architectures that enhance performance, latency, throughput, and efficiency.Optimize our codebase and computing resources (e.g., GPUs) to maximize hardware FLOPs, bandwidth, and memory usage.Extend orchestration frameworks (e.g., Kubernetes, Ray, SLURM) for distributed inference, evaluation, and large-batch serving.Establish standards for reliability, observability, and reproducibility throughout the inference stack.Publish and share insights through internal documentation, open-source libraries, or technical reports that further the field of scalable AI infrastructure.