Software Engineer - Training Infrastructure

ABOUT BASETENBaseten 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, 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 ROLEAs 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 stackEXAMPLE INITIATIVESTake a look at what we’ve built so far:Overview of the product so farTraining docs overviewStory of the Training productResearch we've doneRESPONSIBILITIESDesign 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 solutionsDesign and architect a global training schedulerDesign and architect reinforcement learning systems and continuous learning pipelinesDrive long-term improvements to improve reliability of systems and velocity of developmentPartner closely with SRE and Capacity teams to unlock state of the art training infrastructureMake critical architectural decisions balancing performance with system reliabilityLead technical discussions and mentor junior engineers on infrastructure best practicesContribute to long-term technical strategy and infrastructure roadmapREQUIREMENTSBachelor’s degree or high in Computer Science or related fieldProficiency in Go, with Python experience a plusDeep expertise with Kubernetes in production environmentsExtensive experience with major cloud providers (AWS, GCP) and neo-cloud providers (Crusoe, DigitalOcean, Nebius) a plusAdvanced understanding of distributed systems concepts and performance tuningProven experience designing observability systemsExperience with ML/AI workloads and MLOps platforms highly valuedNICE TO HAVEExperience with distributed storage systemsExperience with workload orchestration platforms like Temporal or AirflowFamiliarity 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 agentsBENEFITSCompetitive compensation, including meaningful equity.100% coverage of medical, dental, and vision insurance for employee and dependentsFlexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)Paid parental leaveFertility and family-building stipend through CarrotCompany-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 forward-thinking team, we would love to hear from you.At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).