Senior Software Engineer - Model Training

ABOUT BASETEN

Baseten powers inference for the world's most dynamic AI companies, like OpenEvidence, Clay, Mirage, Gamma, Sourcegraph, Writer, Abridge, Bland, and Zed. 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. With our recent $150M Series D funding, backed by investors including BOND, IVP, Spark Capital, Greylock, and Conviction, we’re scaling our team to meet accelerating customer demand.

THE ROLE

As a Senior Software Engineer – Model Training at Baseten, you’ll be at the forefront of building the infrastructure that powers large-scale training and fine-tuning of foundation models. You’ll design and implement distributed training systems, optimize GPU utilization, and create scalable pipelines that enable Baseten and our customers to adapt models efficiently and reliably. This role is highly technical and hands-on: you’ll own key components of our training stack, collaborate with product and infra teams to surface customer needs, and push forward the state of the art in scalable training infrastructure.

RESPONSIBILITIES

• Design, build, and maintain distributed training infrastructure for large-scale foundation models

• Implement scalable pipelines for fine-tuning and training across heterogeneous GPU/accelerator clusters

• Optimize training performance through techniques like FSDP, DDP, ZeRO, and mixed precision training

• Contribute to frameworks and tooling that make training workflows efficient, reproducible, and developer-friendly

• Collaborate with cross-functional teams (Product, Forward Deployed Engineering, Inference Infra) to ensure training systems meet real-world requirements

• Research and apply emerging techniques in training efficiency and model adaptation, and productionize them in the Baseten platform

• Participate in code reviews, system design discussions, and technical deep dives to maintain a high engineering bar

REQUIREMENTS

• Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent experience

• 5+ years of experience in ML infrastructure, distributed systems, or ML platform engineering, including 2+ years in a tech lead or manager role

• Strong expertise in distributed training frameworks and orchestration (FSDP, DDP, ZeRO, Ray, Kubernetes, Slurm, or similar)

• Hands-on experience building or scaling training infrastructure for LLMs or other foundation models

• Deep understanding of GPU/accelerator hardware utilization, mixed precision training, and scaling efficiency

• Proven ability to lead and mentor technical teams while delivering complex infrastructure projects

• Excellent communication skills, with the ability to bridge technical depth and business needs

NICE TO HAVE

• Experience building APIs, SDKs, or developer tools for ML workflows

• Familiarity with cluster management and scheduling (Kubernetes, Ray, Slurm, etc.)

• Knowledge of parameter-efficient fine-tuning methods (LoRA, QLoRA) and evaluation pipelines

• Contributions to open-source distributed training or ML infra projects

• Experience with cloud environments (AWS, GCP, Azure) and container orchestration

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 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.