Forward Deployed Engineer (Training)

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

Forward Deployed Engineers work directly with the largest and fastest-growing AI companies in the world, owning their technical outcomes on Baseten and taking on the hardest problems in serving and improving models at scale. The work spans the model lifecycle: inference, post-training, and the systems that tighten the loop between them.

Act as each account's de facto CTO on Baseten, with final accountability for how their workloads are designed, run, and scaled.

Take customer objectives from vague to shipped: frame the problem, define the spec and success criteria, build the PoC, and carry it through to production quickly, using the right tools for the problem.

Design the evals and benchmarks that isolate where quality or performance falls short, then close the gap yourself, whether that means optimizing inference, improving the model through post-training, or reworking the eval itself.

Be the first responder to mission-critical failures including triage, owning the fix directly or route to the owning team and stay accountable until it ships.

Build internal systems so that each engagement is faster than the last. This includes tooling and automation for eval and deployment infrastructure, and the recipes and reference implementations that make the product more self-serve.

Shape the product itself, channeling what your accounts need into the roadmap and shipping fixes and features into Baseten's codebase yourself.

Do all of this across multiple accounts at once, sequencing the work, pulling in the right people at the right time, and keeping customers and internal stakeholders aligned on status and risk.

REQUIREMENTS

Minimum 1-2 years of software engineering experience, shipping and maintaining code in large production systems, ideally with breadth across the stack

Experience debugging complex production issues - working through logs, metrics, and traces to root-cause problems in unfamiliar systems

Confidence owning ambiguous technical problems. This includes triaging, making decisions under uncertainty, and knowing when to pull in other engineers who own the underlying systems

Motivation beyond pure engineering. An interest in wanting to work directly with customers, understand their problems firsthand, and influence the product

Clear communication on complex technical topics, whether you're talking to a customer's engineers or their leadership

Genuine curiosity about AI inference and training, and a drive to become an expert in the infrastructure powering it

Willingness to respond to customers outside regular working hours and participate in an on-call rotation

Excitement about solving problems for some of the largest and fastest-growing companies in the world running mission-critical AI workloads

WHAT YOU'LL BRING

We don't expect any one person to cover all of this - the strongest candidates may spike in one or two of the following:

Depth in a core infrastructure domain such as storage systems or networking (anywhere from the cloud layer to cluster interconnects like InfiniBand and RoCE)

Experience operating distributed compute platforms like Kubernetes, Slurm, or Ray, especially for GPU workloads