200K–400K USD / year
Forward Deployed Engineer (Training)
AIHybrid — San Francisco, California, United States
Published on 2026-08-19
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About this role
Own technical outcomes for major AI customers, taking ambiguous problems from discovery and prototyping through production. Work across inference, model post-training, evaluation, and infrastructure while supporting multiple customer accounts.
About the company
Baseten is an inference cloud that helps companies run and scale AI models in production with flexible infrastructure and developer tooling.
The team
EPD, Engineering, Training
Stack
KubernetesSlurmRayvLLMTensorRT-LLMSGLangPyTorchJAX
What you'll do
- Design, build, and deploy solutions for customer AI workloads.
- Create evaluations and benchmarks, then improve model quality or performance.
- Triage and resolve mission-critical production incidents; remain accountable through resolution.
- Build reusable tooling, automation, and reference implementations for deployments and evaluations.
- Contribute customer-driven fixes and features to Baseten’s codebase and product roadmap.
- Coordinate work across multiple customer accounts and align customers and internal teams on status and risks.
What we're looking for
- At least 1–2 years of software engineering experience shipping and maintaining production code.
- Experience debugging complex production issues using logs, metrics, and traces.
- Ability to own ambiguous technical problems and make decisions under uncertainty.
- Interest in working directly with customers and influencing product direction.
- Clear communication with technical teams and customer leadership.
- Curiosity about AI inference and training.
- Willingness to participate in on-call and respond to customers outside regular working hours.
- Able to work from the San Francisco or New York office three days per week.
Nice to have
- Infrastructure expertise in storage or networking.
- Experience operating distributed compute platforms, especially for GPU workloads.
- Understanding of LLM architectures and modern inference engines.
- Experience profiling and optimizing GPU workloads.
- Hands-on experience with post-training techniques such as SFT and RL, or deep-learning fluency with PyTorch or JAX.
- Operational experience with on-call, incident response, and distributed systems.
Benefits
- Meaningful equity.
- Medical, dental, and vision coverage for employees and dependents in the U.S.
- Flexible paid time off and company-wide winter break.
- Paid parental leave.
- Fertility and family-building stipend.
- Company-facilitated 401(k) in the U.S.