About Our TeamThe Training Runtime team is at the forefront of developing a sophisticated distributed machine-learning training runtime that supports everything from initial research prototypes to cutting-edge model deployments. Our mission is twofold: to enhance the capabilities of researchers and to facilitate large-scale model training. We are creating a cohesive and flexible runtime environment that evolves with researchers as they scale their projects.Our initiatives revolve around three key pillars: optimizing high-performance, asynchronous, zero-copy tensor and optimizer-state-aware data movement; constructing resilient, fault-tolerant training frameworks (including robust training loops, effective state management, resilient checkpointing, and comprehensive observability); and managing distributed processes for long-duration, job-specific uses. By embedding established large-scale functionalities into a user-friendly runtime, we empower teams to iterate rapidly and operate reliably at any scale, working closely with model-stack, research, and platform teams. Our success is measured in terms of both training throughput (the speed at which models are trained) and researcher efficiency (the speed at which concepts transform into experiments and products).About the PositionAs a Machine Learning Framework Engineer on our Training team, you will be pivotal in enhancing the training throughput of our internal framework while empowering researchers to explore innovative ideas. This role demands exceptional engineering skills, including the design, implementation, and optimization of state-of-the-art AI models, as well as writing clean, efficient machine learning code—a task that is often more challenging than it seems. A deep understanding of supercomputer performance metrics will also be critical. Ultimately, every project you undertake will aim to advance the field of machine learning.We seek individuals who are passionate about performance optimization, have a solid grasp of distributed systems, and have an aversion to bugs in their code. Given that our training framework is utilized for extensive runs involving numerous GPUs, any performance enhancements will significantly impact our operations.This position is based in San Francisco, CA, and adheres to a hybrid work model requiring three days in the office each week. We also provide relocation assistance for new hires.Key Responsibilities:Implement advanced techniques within our internal training framework to maximize hardware efficiency during training sessions.Conduct profiling and optimization of our training framework to enhance performance.Collaborate with researchers to facilitate the development of next-generation machine learning models.You Will Excel in This Role If You:Possess a strong passion for optimizing system performance.Have a profound understanding of distributed systems and their complexities.Demonstrate meticulous attention to detail, especially in code quality and debugging.

OverviewPluralis Research is at the forefront of Protocol Learning, innovating a decentralized approach to train and deploy AI models that democratizes access beyond just well-funded corporations. By aggregating computational resources from diverse participants, we incentivize collaboration while safeguarding against centralized control of model weights, paving the way for a truly open and cooperative environment for advanced AI.We are seeking a talented Machine Learning Training Platform Engineer to design, develop, and scale the core infrastructure that powers our decentralized ML training platform. In this role, you will have ownership over essential systems including infrastructure orchestration, distributed computing, and service integration, facilitating ongoing experimentation and large-scale model training.ResponsibilitiesMulti-Cloud Infrastructure: Create resource management systems that provision and orchestrate computing resources across AWS, GCP, and Azure using infrastructure-as-code tools like Pulumi or Terraform. Manage dynamic scaling, state synchronization, and concurrent operations across hundreds of diverse nodes.Distributed Training Systems: Design fault-tolerant infrastructure for distributed machine learning, including GPU clusters, NVIDIA runtime, S3 checkpointing, large dataset management and streaming, health monitoring, and resilient retry strategies.Real-World Networking: Develop systems that simulate and manage real-world network conditions—such as bandwidth shaping, latency injection, and packet loss—while accommodating dynamic node churn and ensuring efficient data flow across workers with varying connectivity, as our training occurs on consumer nodes and non-co-located infrastructure.

Join DoorDash as an Engineering Manager to lead our Backend Frameworks team, where you will shape the technical vision and drive the development of robust backend application frameworks for all engineering teams. Collaborate with senior engineers and stakeholders to enhance platform reliability, velocity, and efficiency. Your role will entail defining and building standards that empower engineers, addressing immediate challenges while anticipating future needs. This position influences our Python, Go, and Kotlin frameworks, combining greenfield projects with established successes. Ideal candidates will be located in San Francisco, Sunnyvale, Seattle, Los Angeles, or New York.

OverviewPluralis Research is at the forefront of innovation in Protocol Learning, specializing in the collaborative training of foundational models. Our approach ensures that no single participant ever has or can obtain a complete version of the model. This initiative aims to create community-driven, collectively owned frontier models that operate on self-sustaining economic principles.We are seeking experienced Senior or Staff Machine Learning Engineers with over 5 years of expertise in distributed systems and large-scale machine learning training. In this role, you will design and implement a groundbreaking substrate for training distributed ML models that function effectively over consumer-grade internet connections.

At Runway ML, we are revolutionizing the intersection of art and science through innovative AI technology. Our mission is to build sophisticated world models that transcend traditional artificial intelligence limitations. We believe that to tackle the most pressing challenges—such as robotics, disease, and scientific breakthroughs—we need systems that can learn from experiences just like humans do. By simulating these experiences, we can expedite progress in ways that were previously unimaginable.Our diverse and driven team consists of creative thinkers who are passionate about pushing boundaries and achieving the extraordinary. If you share this ambition and are eager to contribute to our groundbreaking work, we invite you to join us.About the Role*We are open to hiring remotely across North America. We also have offices in NYC, San Francisco, and Seattle.We are on the lookout for a highly skilled and intellectually inquisitive Technical Accounting Manager to be our go-to authority on intricate accounting issues. This position offers significant visibility and is ideal for a professional adept at interpreting complex accounting guidelines, formulating sound conclusions, and translating technical insights into practical accounting practices.

About Sygaldry Technologies Sygaldry Technologies develops quantum-accelerated AI servers in San Francisco, focusing on faster AI training and inference. By combining quantum technology with artificial intelligence, the team addresses challenges in computing costs and energy efficiency. Their AI servers integrate multiple qubit types within a fault-tolerant system, aiming for a balance of cost, scalability, and speed. The company values optimism, rigor, and a drive to solve complex problems in physics, engineering, and AI. Role Overview: ML Infrastructure Engineer The ML Infrastructure Engineer joins the AI & Algorithms team, which includes research scientists, applied mathematicians, and quantum algorithm specialists. This role centers on building and maintaining the compute infrastructure that powers advanced research. The systems you build will support reliable GPU access, reproducible experiments, and scalable workloads, so researchers can focus on their core work without needing deep cloud expertise. Expect to design and manage compute platforms for a range of tasks, including quantum circuit simulation, large-scale numerical optimization, model training, tensor network contractions, and high-throughput data generation. These workloads span multiple cloud providers and on-premises GPU servers. Key Responsibilities Develop compute abstractions for diverse workloads, such as GPU-accelerated simulations, distributed training, high-throughput CPU jobs, and interactive analyses using frameworks like PyTorch and JAX. Set up infrastructure to support experiment tracking and reproducibility. Create developer tools that make cloud computing feel local, streamlining environment setup, job submission, monitoring, and artifact management. Scale experiments from single-GPU prototypes to large, multi-node production runs. Multi-Cloud GPU Orchestration Design orchestration strategies for workloads across multiple cloud providers, optimizing job routing for cost, availability, and capability. Monitor and improve cloud spending, keeping track of credit balances, burn rates, and expiration dates.

At Sciforium, we are at the forefront of AI infrastructure, dedicated to the development of advanced multimodal AI models and an innovative serving platform that emphasizes high efficiency. With substantial funding and direct collaboration from AMD, our team is rapidly expanding to create the complete stack for pioneering AI models and dynamic real-time applications.Role OverviewThis position provides a distinct opportunity to engage with the fundamental systems that drive Sciforium's multimodal AI models. You will play a crucial role in constructing the model serving platform, working with C++, Python, runtime execution, and distributed infrastructure to design a swift, dependable engine for real-time AI applications.You will acquire practical experience in performance engineering, discover how large AI models are optimized and deployed at scale, and collaborate closely with ML researchers and seasoned systems engineers. If you thrive in low-level programming and are passionate about performance, this role offers both impactful contributions and significant growth opportunities.

About tvScientific tvScientific is the premier CTV advertising platform exclusively tailored for performance marketers. Our innovative approach harnesses vast data and state-of-the-art science to automate and enhance TV advertising, ultimately driving impactful business results. Our platform seamlessly integrates media buying, optimization, measurement, and attribution into one powerful, efficient solution. Developed by industry veterans with extensive backgrounds in programmatic advertising, digital media, and ad verification, our CTV performance platform is designed to help advertisers confidently scale their business. We are currently seeking a Senior MLOps Engineer to join our dynamic, distributed engineering team focused on our Connected TV ad-buying platform, as we expand our Machine Learning capabilities. Having successfully optimized TV ad campaigns, we are poised for massive growth, and we need your expertise to ensure our scalability is both sustainable and effective. As a proud member of Idealab, tvScientific was co-founded by leaders deeply rooted in programmatic advertising and digital media. We empower our clients to purchase ads across the expansive CTV landscape, including platforms such as Hulu, PlutoTV, and the ad-supported tiers of Disney+ and HBO Max. Following our acquisition by Pinterest, we are intensifying our focus on CTV to enhance the performance of search and social advertising.

About UsAt Lemurian Labs, we are dedicated to democratizing AI technology while prioritizing sustainability. Our mission is to create solutions that minimize environmental impact, ensuring that artificial intelligence serves humanity positively. We are committed to responsible innovation and the sustainable growth of AI.We are in the process of developing a state-of-the-art, portable compiler that empowers developers to 'build once, deploy anywhere.' This technology ensures seamless cross-platform integration, allowing for model training in the cloud and deployment at the edge, all while maximizing resource efficiency and scalability.If you are passionate about scaling AI sustainably and are eager to make AI development more powerful and accessible, we invite you to join our team at Lemurian Labs. Together, we can build a future that is innovative and responsible.The RoleWe are seeking a Senior ML Performance Engineer to take charge of designing and leading our Performance Testing Platform from inception. In this pivotal role, you will be recognized as the technical expert in measuring, validating, and enhancing the performance of large language models (including Llama 3.2 70B, DeepSeek, and others) prior to and following compiler optimization on cutting-edge GPU architectures.This is a critical position that will significantly impact our product quality and customer success. You will work at the intersection of Machine Learning systems, GPU architecture, and performance engineering, constructing the infrastructure that substantiates the value of our compiler.

About FaireFaire is a cutting-edge online wholesale marketplace driven by the belief that the future is local. Independent retailers around the world generate more revenue than giants like Walmart and Amazon combined, yet individually, they often struggle against these behemoths. At Faire, we harness the power of technology, data, and machine learning to connect this vibrant community of entrepreneurs globally. Imagine your favorite local boutique; we empower them to discover and sell exceptional products from around the world. With the right tools and insights, we aim to level the playing field, allowing small businesses to compete effectively with large retail chains and e-commerce platforms.By fostering the growth of independent businesses, Faire is making a positive economic impact in local communities worldwide. We’re in search of intelligent, resourceful, and passionate individuals to join us in driving the shop-local movement. If you share our belief in community, we would love to welcome you to ours.About this Role:We are on the lookout for a Principal ML / AI Engineer to serve as a company-wide technical thought leader and practitioner in shaping the future of Data and AI at Faire. This unique opportunity allows you to influence broad technical strategies across data, engineering, and product while engaging directly with pioneering AI research and applications. This role will report directly to the CTO of Faire.Your Responsibilities:Shape the AI Vision – Collaborate with product, design, strategy & analytics, machine learning, and the wider engineering leadership to define how AI can unlock transformational value for Faire’s retailers and brands. Provide thought leadership to guide company-wide priorities, particularly focusing on product strategy and key investment areas.Prototype and Unblock – Lead the development and implementation of AI systems (such as LLM fine-tuning, RLHF, agent frameworks, etc.) that illustrate what’s achievable and promote adoption across teams. Act as a “super individual contributor” who can delve deeply into technical challenges, enabling the engineering organization to advance quickly with AI and amplify both development and impact.Architect the AI-Ready Stack – Design Faire’s technical ecosystem, encompassing event logging, data warehouses, feature stores, and model serving, to ensure our infrastructure is AI-ready, scalable, and optimized for rapid experimentation.

Join Scale AI's ML platform team (RLXF) as a Machine Learning Research Engineer, where you will play a pivotal role in developing our advanced distributed framework for training and inference of large language models. This platform is vital for enabling machine learning engineers, researchers, data scientists, and operators to conduct rapid and automated training, as well as evaluation of LLMs and data quality.At Scale, we occupy a unique position in the AI landscape, serving as an essential provider of training and evaluation data along with comprehensive solutions for the entire ML lifecycle. You will collaborate closely with Scale's ML teams and researchers to enhance the foundational platform that underpins our ML research and development initiatives. Your contributions will be crucial in optimizing the platform to support the next generation of LLM training, inference, and data curation.If you are passionate about driving the future of AI through groundbreaking innovations, we want to hear from you!

About Our TeamAt OpenAI, our Hardware organization is pioneering the development of cutting-edge silicon and system-level solutions tailored to meet the distinctive needs of advanced AI workloads. We are dedicated to building the next generation of AI silicon, collaborating closely with software engineers and research partners to co-design hardware that integrates seamlessly with our AI models. Our mission includes not only delivering high-quality, production-grade silicon for OpenAI's supercomputing infrastructure but also creating custom design tools and methodologies that foster innovation and enable hardware optimized specifically for AI applications.About the RoleWe are on the lookout for a talented Research Hardware Co-Design Engineer to operate at the intersection of model research and silicon/system architecture. In this role, you will play a critical part in shaping the numerics, architecture, and technological strategies for the future of OpenAI's silicon in collaboration with both Research and Hardware teams.Your responsibilities will include diagnosing discrepancies between theoretical performance and real-world measurements, writing quantization kernels, assessing the risks associated with numerics through model evaluations, quantifying system architecture trade-offs, and implementing innovative numeric RTL. This is a hands-on position for individuals who are passionate about tackling challenging problems, seeking practical solutions, and driving them to production. Strong prioritization and transparent communication skills are vital for success in this role.Location: San Francisco, CA (Hybrid: 3 days/week onsite)Relocation assistance available.Key Responsibilities:Enhance our roofline simulator to monitor evolving workloads and deliver analyses that quantify the impact of architectural decisions, supporting technology exploration.Identify and resolve discrepancies between performance simulations and actual measurements; effectively communicate root causes, bottlenecks, and incorrect assumptions.Develop emulation kernels for low-precision numerics and lossy compression techniques, equipping Research with the insights needed to balance efficiency with model quality.Prototype numeric modules by advancing RTL through synthesis; either hand off innovative numeric solutions cleanly or occasionally take ownership of an RTL module from start to finish.Proactively engage with new ML workloads, prototype them using rooflines and/or functional simulations, and initiate evaluations of new opportunities or risks.Gain a holistic understanding of the transition from ML science to hardware optimization, breaking down this comprehensive objective into actionable short-term deliverables.Foster collaborative relationships across diverse teams with varying goals and expertise, ensuring that progress remains unimpeded.Clearly articulate design trade-offs with explicit assumptions and rationale.

About Us:At Parafin, our mission is to empower small businesses to thrive in today's competitive landscape. We understand that small businesses form the backbone of our economy, yet they often face challenges in accessing essential financial resources. Our innovative technology streamlines access to vital financial tools directly on the platforms they already utilize for sales. Partnering with industry leaders such as DoorDash, Amazon, Worldpay, and Mindbody, we provide small businesses with fast, flexible funding, efficient spend management, and effective savings solutions through simple integrations. Parafin manages the complexities of capital markets, underwriting, servicing, compliance, and customer support to ensure seamless experiences for our partners and their small business clients.We are composed of a dynamic team of innovators with backgrounds from top firms like Stripe, Square, Plaid, Coinbase, Robinhood, and CERN, all driven by a passion for developing tools that facilitate small business success. Backed by esteemed venture capitalists including GIC, Notable Capital, Redpoint Ventures, Ribbit Capital, and Thrive Capital, Parafin stands as a Series C company with over $194M raised in equity and $340M in debt facilities. Join us in shaping a future where every small business has access to the financial tools they need.About The PositionWe are on the lookout for a skilled Software Engineer to join our Infrastructure team and spearhead the advancement of our Machine Learning (ML) Platform. This pivotal role is essential for constructing reliable, scalable, and developer-centric systems for model experimentation, training, evaluation, inference, and retraining that drive underwriting and other ML-powered products for small businesses.As a Software Engineer, you will design, build, and maintain the core frameworks and platforms that empower data scientists to deploy high-quality models into production efficiently and safely. You'll work closely with Data Science and Platform Engineering, taking ownership of the ML platform from end-to-end, and develop both batch and real-time underwriting infrastructure.What You'll DoTransform notebooks into reliable software. Break down data scientist training and inference notebooks into reusable, well-tested components (libraries, pipelines, templates) with clear interfaces and documentation.Develop user-friendly ML abstractions. Create SDKs, CLIs, and templates that simplify the definition of features, model training and evaluation, and deployment to batch or real-time targets with minimal boilerplate.Construct our real-time ML inference platform. Establish and scale low-latency model serving capabilities.Enhance batch ML inference processes. Optimize scheduling, parallelism, cost controls, and observability to improve efficiencies.

Join Sonsoft Inc. as a Java/J2EE Framework Developer in the vibrant city of San Francisco. We are seeking a talented and driven individual to help us build robust applications using Java and J2EE frameworks. You will collaborate with a dynamic team, working on innovative projects that impact our clients and the industry.

Draup, based in San Francisco, is an AI company with Series A funding that builds intelligence solutions for large enterprises. The platform analyzes over 1 billion job descriptions and 850 million professional profiles, serving more than 250 enterprise clients, including several Fortune 10 companies. Draup’s data comes from more than 100 labor databases, supporting clients with deep workforce insights. Role overview The engineering team in Silicon Valley is expanding. Draup seeks experienced AI/ML Engineers interested in advancing both research and product development in artificial intelligence. What you will do Develop and maintain production-grade large language model (LLM) pipelines and agentic workflows. Design and enhance retrieval-augmented generation (RAG) architectures at scale, using vector databases such as Pinecone, FAISS, and Weaviate. Implement advanced agentic systems with tools like LangGraph or LlamaIndex, focusing on tool use, multi-agent coordination, and reasoning loops. Lead prompt engineering, manage model versioning, oversee evaluation (including RAGAS and DeepEval), and instrument LLMOps. Integrate AI features into large-scale data pipelines, ensuring observability in production and compliance with guardrails. Location This position is based in San Francisco, CA (Silicon Valley).

About Pinterest:At Pinterest, our platform inspires millions of people around the globe to explore creative ideas, envision new possibilities, and create lasting memories. We are dedicated to providing the inspiration needed to build a fulfilling life, starting with the talented individuals who drive our product development.Join us in a career that sparks innovation for millions, transforms passion into opportunities for growth, and celebrates the diverse experiences of our team members, all while enjoying the flexibility to perform at your best. Building a career you love is within reach.Position Overview:We are looking for a Senior Engineering Manager to spearhead our AI/ML Serving Platform team, which develops the core tools and infrastructure utilized by numerous AI/ML engineers across Pinterest. This includes systems for recommendations, advertisements, visual search, notifications, and trust and safety. Our goal is to enhance the efficiency, quality, and speed of AI/ML systems, ensuring they are production-ready and reliable for iterative model development.Key Responsibilities:Lead the team in driving continuous improvements in advanced model architectures, optimizing resource usage, and boosting AI/ML developer productivity.Establish the technical vision for the team aligned with company and organizational priorities.Mentor and cultivate talent within the team.Qualifications:Proven experience in managing engineering teams with diverse cross-organizational clients.Expertise in developing large-scale distributed serving systems.Familiarity with AI/ML inference technologies (e.g., PyTorch, TensorFlow) for web-scale online serving.Bachelor's degree in Computer Science or a related field, or equivalent professional experience.

Role Overview Sigma Computing is building the next generation of data interaction. The platform lets users explore and analyze billions of data rows in seconds, all within a familiar spreadsheet-like interface. Sigma aims to make it simple to analyze, present, and build data-driven applications at scale. AI is central to Sigma's vision for the future. The company is expanding its use of artificial intelligence to help users build in Sigma, surface insights, and make decisions faster. What You Will Do As a Senior AI/ML Engineer, join a team focused on shaping the AI architecture behind Sigma's platform. This work directly impacts thousands of enterprises that depend on Sigma for their data workflows. The team is responsible for designing and implementing the systems that will power Sigma's AI-driven features for years to come. Location This position is based in San Francisco, CA.

HeartFlow, Inc. is at the forefront of medical technology, innovating the diagnosis and management of coronary artery disease—the leading cause of death globally—through state-of-the-art technology. Our flagship offering, the HeartFlow FFRCT Analysis, is an AI-powered, non-invasive cardiac test that adheres to the ACC/AHA Chest Pain Guidelines. It generates a detailed, color-coded 3D model of a patient’s coronary arteries, illustrating the implications of blockages on heart blood flow. HeartFlow stands as the premier AI-based non-invasive integrated heart care solution throughout the CCTA pathway, assisting clinicians in identifying coronary artery stenoses (RoadMap™ Analysis), evaluating coronary blood flow (FFRCT Analysis), and characterizing and quantifying coronary atherosclerosis (Plaque Analysis). Our expanding range of products and dedicated team are committed to transforming precision heart care.As a publicly traded entity (HTFL), HeartFlow has garnered international acclaim for its groundbreaking contributions to healthcare innovation, receiving endorsements from medical societies globally. We are cleared for use in the US, UK, Europe, Japan, and Canada, having positively impacted over 500,000 patients worldwide.We are currently seeking an enthusiastic Software Engineering Intern to join our Machine Learning (ML) Engineering and MLOps team at our San Francisco office. In this role, you will collaborate with experienced engineers and research scientists on a defined project that contributes to HeartFlow's transition towards a cloud-native, unified ML pipeline. This internship will provide you with hands-on experience in cardiac AI technologies, software architecture, and validation methodologies essential for running algorithms at scale. You will be assigned to a project encompassing computational geometry, microservices, and inference pipeline architectures.

Merge Labs is a pioneering research laboratory dedicated to uniting biological intelligence with artificial intelligence, aiming to enhance human potential, autonomy, and overall experience. We are innovating groundbreaking methods for brain-computer interfaces that facilitate high-bandwidth interactions with the brain, seamlessly integrate advanced AI, and ensure safety and accessibility for all users.About the Team:At Merge Labs, we are developing the future of brain-computer interfaces through the integration of cutting-edge advancements in synthetic biology, neuroscience, AI, and non-invasive imaging. Our cross-functional data and software engineering team collaborates closely with wet-lab scientists, automation engineers, and data scientists to construct a digital infrastructure that expedites molecular discoveries and optimizes device performance.About the Role:We are seeking a Senior / Principal ML Engineer to lead the development and ownership of the digital infrastructure supporting Merge's extensive computational operations. In this role, you will design distributed training and inference systems, experiment tracking, and deployment frameworks, empowering data scientists to swiftly iterate on models encompassing de-novo molecular design, biophysical modeling, signal processing, and computer vision. Your architectural contributions will transform research prototypes into production-ready systems, enhancing the speed, rigor, and fluidity of every computational scientist's workflow.Key Responsibilities:Develop the scientific and engineering framework for active learning and closed-loop optimization, including data ETL, machine learning modeling, and library architecture.Work alongside computational scientists to establish achievable optimization goals and encode domain-specific knowledge and constraints.Create model registries, evaluation frameworks, and automated reporting systems for benchmarking and experimental comparisons.Implement CI/CD pipelines and resource orchestration using tools like Kubernetes, Ray, or Slurm.Define and manage the ML engineering roadmap, providing mentorship to other computational scientists while establishing best practices for code quality, testing, and reproducibility.