Software Engineer - Training Infrastructure

Software Engineer - Training Infrastructure ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $300M Series E https://www.baseten.co/blog/announcing-baseten-s-300m-series-e/, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Join us and help build the platform engineers turn to to ship AI products. THE ROLE As a Software Engineer at on the Training Infrastructure team, you'll architect and lead development of our training platform, supporting top tier research engineers and model developers. You'll make key technical decisions for the infrastructure enabling developers to deploy, scale, and monitor their workloads with high performance and reliability. You’ll own scheduling, storage, networking, reliability, and observability of technical systems in the training stack EXAMPLE INITIATIVES Take a look at what we’ve built so far: - Overview of the product so far https://www.baseten.co/blog/baseten-training-is-ga/#training-is-now-ga - Training docs overview https://docs.baseten.co/training/overview - Story of the Training product https://www.baseten.co/blog/a-q-a-from-inference-to-training-the-inside-story-of-baseten-s-newest-product/ - Research we've done https://www.baseten.co/resources/research/ RESPONSIBILITIES - Design and architect scalable infrastructure systems for our ML training platform (e.g. scheduling, storage, and networking) - Partner closely with developers and research engineers to translate complex training requirements into technical solutions - Design and architect a global training scheduler - Design and architect reinforcement learning systems and continuous learning pipelines - Drive long-term improvements to improve reliability of systems and velocity of development - Partner closely with SRE and Capacity teams to unlock state of the art training infrastructure - Make critical architectural decisions balancing performance with system reliability - Lead technical discussions and mentor junior engineers on infrastructure best practices - Contribute to long-term technical strategy and infrastructure roadmap REQUIREMENTS - Bachelor’s degree or high in Computer Science or related field - Proficiency in Go, with Python experience a plus - Deep expertise with Kubernetes in production environments - Extensive experience with major cloud providers (AWS, GCP) and neo-cloud providers (Crusoe, DigitalOcean, Nebius) a plus - Advanced understanding of distributed systems concepts and performance tuning - Proven experience designing observability systems - Experience with ML/AI workloads and MLOps platforms highly valued NICE TO HAVE - Experience with distributed storage systems - Experience with workload orchestration platforms like Temporal or Airflow - Familiarity or experience with the open source training stack and frameworks (NCCL, PyTorch, Megatron, NemoRL, VeRL, Axolotl, HF Trainier) and distributed training techniques (FSDP, DeepSpeed). - Experience developing AI products, tooling, or agents BENEFITS - Competitive compensation, including meaningful equity. - 100% coverage of medical, dental, and vision insurance for employee and dependents - Generous PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forwar ... (truncated, view full listing at source)