Software Engineer - Training Infrastructure [Remote]

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 $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.

THE ROLE

As a Software Engineer 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:

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 increase 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 higher in Computer Science or related field

• 5+ years of experience

• Proficiency in Go, with

• Deep expertise with Kubernetes in production environments

• Advanced understanding of distributed systems concepts and performance tuning

• Proven experience designing observability systems

• Experience with ML/AI workloads and MLOps platforms

NICE TO HAVE

• Experience with distributed storage systems

• Python experience a plus

• Extensive experience with major cloud providers (AWS, GCP) and neo-cloud providers (Crusoe, DigitalOcean, Nebius)

• 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 Trainer) and distributed training techniques (FSDP, DeepSpeed).

• Experience developing AI products, tooling, or agents

BENEFITS

• Competitive compensation, including meaningful equity

• (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents

• Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)

• Paid parental leave

• Fertility and family-building stipend through Carrot

• (U.S. only) 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 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).