Staff Software Engineer - Machine Learning Infrastructure

As a Technical Staff Member specializing in Machine Learning, you will:Engage in the complete development lifecycle of innovative large-scale deep learning models.Curate datasets, architect solutions, implement algorithms, and train and assess models to enhance our offerings.Work collaboratively with engineers and researchers to convert groundbreaking research into real-world applications.Join us at a pivotal time, take on diverse roles, and contribute to building transformative products from the ground up!

At Gimlet Labs, we are pioneering the development of the first heterogeneous neocloud designed specifically for AI workloads. As the demand for AI systems surges, traditional homogeneous infrastructures face critical limits in power, capacity, and cost. Our innovative platform effectively decouples AI workloads from their hardware foundations, intelligently partitioning tasks and orchestrating them to the most suitable hardware for optimal performance and efficiency. This strategy fosters heterogeneous systems that span multiple vendors and generations, including cutting-edge accelerators, enabling significant enhancements in performance and cost-effectiveness at scale.In addition to this foundational work, Gimlet is establishing a robust neocloud for agentic workloads. Our clients benefit from deploying and managing their workloads via stable, production-ready APIs, without the need to navigate hardware selection or performance optimization intricacies.We collaborate with foundation labs, hyperscalers, and AI-native companies to drive real production workloads capable of scaling to gigawatt-class AI datacenters.We are currently seeking a Member of Technical Staff specializing in ML systems and inference. In this pivotal role, you will be responsible for designing and constructing inference systems that facilitate the execution of complete models in real production environments. You will operate at the intersection of model architecture and system performance to ensure that inference processes are swift, predictable, and scalable.This position is perfect for engineers with a deep understanding of modern model execution and a passion for optimizing latency, throughput, and memory utilization across the entire inference lifecycle.

Shape the Future with AI InnovationAt asari.ai, we are on a mission to empower individuals to create intricate systems and tackle the world's most challenging problems with the aid of advanced AI agents that are both scalable and reliable.Our team boasts a history of publishing award-winning AI research and is supported by elite investors such as Eric Schmidt, Caltech, Jeff Dean, and JP Millon.We thrive in a fast-paced environment, leveraging first-principles thinking and purposeful building. We believe that exceptional outcomes arise when individuals take ownership, grow collectively, and share both the hurdles and victories.Your RoleDesign, train, and assess hybrid AI systems that excel at scale while making optimal trade-offs.Develop scalable data processing and machine learning frameworks.Address min-max challenges: maximizing output while minimizing resources.Enhance our productivity by eliminating operational and tooling constraints.Ideal Candidate ProfileYou find motivation in resolving complex real-world challenges.You possess a proven background in a technical field such as machine learning, computer science, physics, or mathematics.You demonstrate strong programming skills (in Python, C++) and mathematical proficiency.You exhibit robust conceptual and structured thinking abilities.You communicate clearly and effectively, both verbally and in writing.You are eager and capable of rapid learning.You embody a team-oriented spirit.You can independently organize, plan, prioritize, and execute tasks.You are driven by excellence, ownership, and a proactive mindset.Preferred QualificationsExperience with open-source projects, published research, or demonstrated expertise in machine learning.Familiarity with applying deep learning, reinforcement learning, unsupervised learning, and related techniques to large-scale problems.Experience with distributed computing and managing large datasets.Compensation and BenefitsAttractive salaryEquity optionsComprehensive health insurance (100% covered), dental (90% covered), and vision (90% covered). Dependent coverage is at 50%.

Tzafon is at the forefront of machine intelligence, operating as a cutting-edge foundation model lab dedicated to building scalable computing systems. With offices in San Francisco, Zurich, and Tel Aviv, we have secured over $12 million in funding to propel our mission of expanding the boundaries of machine intelligence.Our talented team comprises engineers and scientists with extensive expertise in ML infrastructure and research, founded by distinguished IOI and IMO medalists, PhD holders, and alumni from top tech firms such as Google DeepMind, Character, and NVIDIA. We specialize in training models and constructing infrastructure for swarms of agents to automate tasks across real-world environments.In this role, you'll collaborate between our product and post-training teams to deploy Large Action Models that deliver results. Your responsibilities will include building evaluations, benchmarks, and fine-tuning pipelines, as well as defining optimal model behavior and achieving it at scale.

Join Reka as a Member of the Technical Staff in Applied AI!Leverage cutting-edge AI models to tackle intricate real-world challenges.Engage in close collaboration with researchers and fellow team members to explore the latest developments in AI and ML.Partner with our customers to seamlessly integrate our innovative models into their existing technology frameworks.Drive business success with a strong sense of product ownership and accountability.Be part of a pioneering team in a rapidly growing environment, taking on diverse roles.

At Tzafon, we are pioneering the development of scalable computing systems and pushing the boundaries of machine intelligence with our foundation model lab. Located in vibrant cities such as San Francisco, Zurich, and Tel Aviv, we have successfully secured over $12 million in funding to fuel our mission of expanding the horizons of AI technology.Our dynamic team comprises engineers and scientists with extensive expertise in machine learning infrastructure and research. Founded by IOI and IMO medalists, PhDs, and seasoned professionals from top tech firms, we specialize in training advanced models and constructing robust infrastructures to automate tasks across various real-world scenarios.In this role, you will collaborate closely with our product and post-training teams to deploy Large Action Models that drive impactful results. Your responsibilities will include building evaluation frameworks, establishing benchmarks, and creating fine-tuning pipelines to ensure optimal model performance.

Join the team at Mirendil as a Member of Technical Staff specializing in Machine Learning Systems. In this role, you will leverage your expertise to develop innovative solutions that enhance our ML frameworks and contribute to groundbreaking projects in the AI space. Collaborate with top talent in a dynamic environment that promotes creativity and technical excellence.

Our VisionAt ReflectionAI, we strive to create open superintelligence and ensure its accessibility for everyone.Our team is dedicated to developing open weight models for individuals, organizations, and even nations. Our collective expertise comes from leading AI institutions such as DeepMind, OpenAI, Google Brain, Meta, Character.AI, Anthropic, and more.Role OverviewConduct research and develop solutions focusing on algorithms, scaling laws, data processing, optimizers, and model architecture.Design and execute scientific experiments to enhance our understanding of scaling large language models and improving data efficiency.Apply cutting-edge techniques from the deep learning literature to our projects.Independently lead small research initiatives while collaborating on larger projects.Enhance our training infrastructure for optimal scaling efficiency.Contribute across the entire technology stack, from low-level optimizations to high-level model design.Your ProfilePossess a graduate degree (MS or PhD) in Computer Science, Machine Learning, or a related field.Demonstrate strong software engineering skills with experience in large-scale systems development.Have prior experience with large-scale ETL processes and preparing training data.Possess a deep understanding of large-scale machine learning, specifically regarding language models, distributed training, and scaling.Be proficient in Python and familiar with deep learning frameworks, preferably PyTorch.Effectively navigate the trade-offs between research goals and practical engineering challenges.Excel in a fast-paced, high-agency startup culture with a proactive approach.Exhibit strong communication skills and a collaborative mindset.Show a passion for pushing the boundaries of intelligence.What We Provide:We believe that building truly open superintelligence starts with a solid foundation. Joining ReflectionAI means being part of a tightly-knit, highly talented team, where you will help shape our future and redefine the landscape of open foundational models.

Our MissionAt Reflection AI, our goal is to develop open superintelligence and make it universally accessible.We are pioneering open weight models tailored for individuals, agents, enterprises, and even entire nations. Our diverse team comprises talented AI researchers and industry veterans from prestigious organizations such as DeepMind, OpenAI, Google Brain, Meta, Character.AI, Anthropic, and many more.Role OverviewConstruct and enhance distributed training systems that drive the pre-training of cutting-edge models.Collaborate with research teams to design and execute extensive training runs for foundational models.Create infrastructure that facilitates efficient training across thousands of GPUs leveraging contemporary distributed training frameworks.Enhance training throughput, stability, and efficiency for extensive model training tasks.Work closely with pre-training researchers to convert experimental concepts into scalable, production-ready training systems.Boost performance of distributed training tasks through optimization of communication, memory management, and GPU utilization.Develop and maintain training pipelines that accommodate large-scale datasets, checkpointing, and iterative experiments.Identify and resolve performance bottlenecks within distributed training systems, including model parallelism, GPU communication, and training runtime environments.Contribute to the creation of systems that promote swift experimentation and iteration on novel training methods.

Join us as a Backend Engineer - Member of Technical Staff at fuku, located in the vibrant tech hub of San Francisco, CA. In this full-time role, you will play a pivotal part in revolutionizing compliance processes for banks and fintechs through the development of advanced AI agents. These agents mimic human analysts to navigate the web and automate critical compliance functions, including AML, KYC, KYB, and transaction monitoring. With a compensation range of $150,000 to $280,000 plus competitive equity, we are offering a remarkable opportunity for high-impact contributions within our rapidly growing engineering team.Your mission will be to build and enhance the backend and machine learning systems that inform our AI agents. You’ll tackle complex challenges like developing browser agents that interact with legacy systems, creating a global risk graph, and ensuring compliance decisions are made at lightning speed. You will have the freedom to own features from design through deployment, working in an environment that values autonomy and innovation.This role is ideal for engineers who are eager to push the boundaries of AI application, seek minimal meetings, and thrive in a small, dynamic team. If you are passionate about technology that makes a real-world impact, we want to hear from you!

Join Our Team as a Member of Technical StaffAt Eragon, we are on the forefront of innovation, seeking a dedicated Member of Technical Staff to spearhead the development and deployment of state-of-the-art AI systems. This exciting role invites you to collaborate across modeling, systems, and product teams to transform innovative concepts into impactful real-world applications.Your Key ResponsibilitiesSystem Development & Deployment: Design, integrate, and launch AI-driven systems into production environments.Model Development: Refine, assess, and implement machine learning models in practical scenarios.Systems Engineering: Architect scalable pipelines for training, inference, and data processing workflows.Performance Optimization: Enhance system latency, throughput, cost-effectiveness, and reliability.Data & Infrastructure: Manage large-scale datasets and ensure seamless integration with internal tools and APIs.Cross-Functional Collaboration: Collaborate with product, research, and design teams to deliver complete features.Evaluation & Monitoring: Establish evaluation frameworks, monitoring tools, and feedback mechanisms.What You Bring to the TableEducation: A Bachelor's or Master's degree in Computer Science, Engineering, or a related field.Technical Skills: Proficient in Python and familiar with modern engineering or machine learning frameworks.Production Experience: Proven experience in building and deploying systems in production settings.Systems Knowledge: Knowledge of data pipelines, APIs, and cloud infrastructures (AWS, GCP).Practical ML Experience: Hands-on experience with machine learning models or data-centric systems.Preferred QualificationsExperience with deploying or scaling machine learning systems in production environments.Familiarity with large language models, agents, or workflow automation systems.Experience in distributed systems or large-scale infrastructure.A background in fast-paced or early-stage environments.

Our MissionAt Reflection AI, our goal is to create open superintelligence and ensure its accessibility for everyone.We are pioneering open weight models for various users, including individuals, enterprises, and even nation-states. Our talented team comprises AI researchers and industry veterans from leading organizations such as DeepMind, OpenAI, Google Brain, Meta, Character.AI, and Anthropic.Role OverviewDevelop systems that convert robust pre-trained models into aligned and versatile agents.Lead research and engineering efforts to advance post-training practices, focusing on data curation and large-scale optimization.Create data generation frameworks, reward models, reinforcement learning algorithms, and techniques for inference-time scaling.Collaborate with both pre-training and post-training teams to achieve significant enhancements in model capabilities.Help refine our understanding of how large models learn to reason, follow instructions, and evolve through reinforcement learning.Your ProfileSolid grasp of machine learning principles with hands-on experience in large-scale LLM training.Proficient engineering skills, with the ability to navigate intricate ML codebases and distributed systems.Experience in enhancing model performance through data, reward modeling, or reinforcement learning techniques.Track record of leading ambitious research or engineering projects resulting in measurable improvements.Thrives in a dynamic, high-agency startup atmosphere; oriented towards action and clarity in execution.Ability to work seamlessly across research and infrastructure boundaries.Excellent communication skills and a collaborative mindset.Driven by a passion for pushing the boundaries of intelligence.What We Provide:At Reflection AI, we believe that to truly build open superintelligence, it must be rooted in a strong foundation. By joining us, you will contribute to building from the ground up within a compact, highly skilled team. Together, we will shape the future of our company and the landscape of open foundational models.We aim for you to accomplish the most impactful work of your career, with the assurance that you and your loved ones are well-supported.

Technical Staff Member in Applied AIAbout the OpportunityWe are seeking a highly skilled Technical Staff Member specializing in generative modeling to bridge the gap between our advanced models and the clients who rely on them. You will collaborate with a diverse team of machine learning experts, protein engineers, and biologists to revolutionize biological control and disease treatment. Your role will involve gaining a comprehensive understanding of our proprietary generative models and leveraging that expertise to deploy, adapt, and optimize these models in client environments, particularly within the pharmaceutical and biotech industries.This hybrid position requires a research-oriented mindset to deeply understand our models, paired with the communication skills necessary to translate that knowledge into production systems that yield scientific value for our collaborators.About UsAt Latent Labs, we are pioneering frontier models that decode the fundamentals of biology. Our ambitious goals are driven by curiosity and a commitment to scientific excellence. Prior to founding Latent Labs, our team co-developed DeepMind's Nobel Prize-winning AlphaFold, innovated latent diffusion, and created groundbreaking lab data management systems along with high-throughput protein screening platforms. Here, you will work alongside some of the brightest minds in generative AI and biology.We value interdisciplinary collaboration, continuous learning, and teamwork. Our team offsites foster a culture of trust and connection between our London and San Francisco offices. We are looking for innovators who are passionate about solving complex problems and making a positive global impact. Join us on our ambitious mission.Your QualificationsExpertise in Machine Learning: You are a proficient ML researcher with a strong background in generative modeling, evidenced by your contributions to notable open-source projects, impactful product launches, or significant publications in leading venues such as NeurIPS, ICML, ICLR, or Nature. You possess a deep understanding of generative model architectures, training dynamics, and inference behavior.Proficient ML Developer: You produce robust, tested, and maintainable ML code. Your experience includes using version control and code review systems. You are adept at rapid prototyping while also being able to write elegant production code. Additionally, you have experience in building systems that deploy large models via APIs and executing inference tasks in production environments.

At Catalog, we are pioneering the commerce infrastructure for AI—creating the essential framework that enables digital agents to not only explore the web but also comprehend, analyze, and engage with products. Our innovations drive the future of AI-driven shopping experiences, fundamentally transforming how consumers discover and purchase items online.Role OverviewAs a Technical Staff Member, you will be instrumental in developing core systems, shaping our engineering culture, and transitioning our vision from prototype to a robust platform. This role requires full-stack expertise and a commitment to owning and resolving challenges from start to finish.Who You AreYou have experience creating beloved and trusted products from the ground up.You combine technical proficiency with a keen product sense and data-driven intuition.You are well-versed in AI technologies.You prioritize speed, write clean code, and ensure thorough instrumentation.You seek a high level of ownership within a small, talent-rich team based in San Francisco.Challenges You Will TackleDevelop and deploy agentic-search APIs that deliver structured and real-time product data in milliseconds.Build checkout systems enabling agents to conduct transactions with any merchant.Create an embeddings and retrieval layer that optimizes recall, precision, and cost efficiency.Establish a product graph and ranking pipeline that adapts based on actual user outcomes.Preferred QualificationsProven experience shipping data-centric products in a live environment.Experience with recommendation systems or information retrieval methodologies.Familiarity with API development, search indexing, and data pipeline construction.Our Work CultureWe operate with a small, high-trust, and highly motivated team, fostering an environment of in-person collaboration in North Beach, San Francisco. Our process involves debate, decision-making, and execution.If your profile aligns with our needs, we will contact you to arrange 2-3 brief technical interviews, followed by an onsite meeting in our office where you will collaborate on a small project, exchange ideas, and meet the team.

Join Our TeamAt Liquid AI, we are not just creating AI models; we are revolutionizing the very fabric of intelligence. Originating from MIT, our objective is to develop efficient AI systems across all scales. Our Liquid Foundation Models (LFMs) excel in environments where others falter—on-device, at the edge, and under real-time constraints. We are not simply refining existing concepts; we are pioneering the future of AI.We recognize that exceptional talent drives remarkable technology. The Liquid team is a collective of elite engineers, researchers, and innovators dedicated to crafting the next generation of AI solutions. Whether you are designing model architectures, enhancing our development platforms, or facilitating enterprise integrations, your contributions will significantly influence the evolution of intelligent systems.While San Francisco and Boston are preferred locations, we welcome applicants from other regions within the United States.

Join our dynamic team at Adyen as a Technical Staff Member in San Francisco! We are seeking innovative minds passionate about technology and problem-solving. In this role, you will collaborate with cross-functional teams to craft solutions that enhance our services and improve customer experiences.

At Magic, we are driven by our mission to develop safe Artificial General Intelligence (AGI) that propels humanity forward in addressing the most critical challenges. We firmly believe that the future of safe AGI lies in automating research and code generation, allowing us to enhance models and tackle alignment issues more effectively than humans alone can manage. Our innovative approach combines cutting-edge pre-training, domain-specific reinforcement learning (RL), ultra-long context, and efficient inference-time computation to realize this vision.Position OverviewAs a Software Engineer within the Inference & RL Systems team, you will play a pivotal role in designing and managing the distributed systems that enable our models to function seamlessly in production, supporting extensive post-training workflows.This position operates at the intersection of model execution and distributed infrastructure, focusing on systems that influence inference latency, throughput, stability, and the reliability of RL and post-training training loops.Our long-context models impose significant execution demands, including KV-cache scaling, managing memory constraints for lengthy sequences, batching strategies, long-horizon trajectory rollouts, and ensuring consistent throughput under real-world workloads. You will be responsible for the infrastructure that ensures both production inference and large-scale RL iterations are efficient and dependable.Key ResponsibilitiesCraft and scale high-performance inference serving systems.Optimize KV-cache management, batching methods, and scheduling processes.Enhance throughput and latency for long-context tasks.Develop and sustain distributed RL and post-training infrastructure.Boost reliability across rollout, evaluation, and reward pipelines.Automate fault detection and recovery mechanisms for serving and RL systems.Analyze and eliminate performance bottlenecks across GPU, networking, and storage components.Collaborate with Kernel and Research teams to ensure alignment between execution systems and model architecture.QualificationsSolid foundation in software engineering and distributed systems.Proven experience in building or managing large-scale inference or training systems.In-depth understanding of GPU execution constraints and memory trade-offs.Experience troubleshooting performance issues in production machine learning systems.Capability to analyze system-level trade-offs between latency, throughput, and cost.

Join our innovative team at liquid-ai as a Member of the Technical Staff specializing in audio applications. As a post-training role, you will have the opportunity to apply your knowledge in cutting-edge audio technologies, contributing to the development of advanced machine learning solutions.This position is ideal for individuals who are eager to work in a collaborative environment and are passionate about audio technology and its applications in artificial intelligence.

Join Our Team at XDOFAt XDOF, we are at the forefront of building the future of robotics by focusing on the critical element that drives innovation: data. Our mission is to create robust data collection systems and annotation pipelines that serve as the backbone of advanced foundation models in robotics.We are seeking a passionate Research Engineer / Scientist to spearhead technical initiatives in the intersection of vision-language models and robotic learning. Your role will involve transforming egocentric and teleoperation video into high-quality training data for VLA models, as well as contributing to the development of these models directly.In addition to pipeline construction, you will delve into research aimed at enhancing the utility of robot data. This includes uncovering new metadata such as contact events, affordance labels, and dynamics priors from video to unlock new capabilities that current methodologies overlook. You will investigate the impact of structured annotations on cross-embodiment transfer, automatic curriculum generation, and the development of world models that predict essential factors for manipulation. In our view, the data layer is not merely a supporting element; it is integral to the research itself.Your ResponsibilitiesDesign and implement vision-language pipelines for processing egocentric and teleoperation video, focusing on structured captioning, temporal grounding, action-conditioned scene understanding, and large-scale semantic annotation.Develop and assess representations that connect visual perception, language, and robotic actions, encompassing VLAs, video predictions, and world models.Create and enhance data curation systems that evaluate the quality, diversity, and comprehensiveness of extensive robot demonstration datasets.Engage with bimanual and high-DoF manipulation data, including real teleoperation footage and simulation-generated rollouts.Collaborate closely with partner labs to identify data requirements and establish a feedback loop between data quality and downstream policy performance.Stay updated on cutting-edge research in VLAs, video foundation models, flow matching, DiT architectures, and egocentric pretraining, translating insights into practical applications.