Forward Deployed Engineer (Training)

About Baseten

Baseten powers mission-critical inference for the world’s most dynamic AI companies, including Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable frontier AI companies 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 when shipping AI products.

The Role

Forward Deployed Engineers work directly with the largest and fastest-growing AI companies, 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.
• Design evals and benchmarks to isolate where quality or performance falls short, then close the gap yourself—whether by optimizing inference, improving the model through post-training, or reworking the eval.
• Be the first responder to mission-critical failures, including triage; own the fix directly or route to the owning team, and stay accountable until it ships.
• Build internal systems so each engagement is faster than the last, including tooling and automation for eval and deployment infrastructure, plus recipes and reference implementations that make the product more self-serve.
• Shape the product by channeling what your accounts need into the roadmap and shipping fixes and features into Baseten’s codebase.
• Execute across multiple accounts at once by sequencing 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, including triaging, making decisions under uncertainty, and knowing when to pull in other engineers who own the underlying systems.
• Motivation beyond pure engineering: interest in working directly with customers, understanding their problems firsthand, and influencing the product.
• Clear communication on complex technical topics with both customers’ engineers and leadership.
• Genuine curiosity about AI inference and training, plus 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 running mission-critical AI workloads.

What You’ll Bring

We don’t expect any one person to cover everything. The strongest candidates may bring depth in one or two of the following:

• Depth in a core infrastructure domain such as storage systems or networking (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.
• A detailed understanding of LLM architectures and modern inference engines like vLLM, TensorRT-LLM, or SGLang.
• The ability to profile and optimize GPU workloads in training or serving.
• Hands-on experience with post-training techniques like SFT and RL, or a broader deep learning background plus fluency in a tensor computation library like PyTorch or JAX.
• Operational depth, including running on-call, leading incident response, and debugging distributed systems under pressure.

Benefits

• Competitive compensation, including meaningful equity.
• 100% coverage of medical, dental, and vision insurance for employees and dependents.
• Flexible PTO policy, including a company wide Winter Break (offices closed from Christmas Eve to New Year’s Day).
• Paid parental leave.
• Fertility and family-building stipend through Carrot.
• Company-facilitated 401(k).
• Exposure to a variety of ML startups, offering learning and networking opportunities.

Apply now to embark on a rewarding journey shaping the future of AI. If you’re motivated and passionate about machine learning, and want to join a collaborative, forward-thinking team, we’d 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).