Forward Deployed Engineer (Training)

Responsibilities

• Own each customer account’s technical outcomes, including workload design, operation, and scaling on Baseten.
• Translate vague customer objectives into specifications, success criteria, proofs of concept, and production solutions.
• Design evaluations and benchmarks, optimize inference, improve models through post-training, and rework evaluations as needed.
• Triage mission-critical failures, own or route fixes, and remain accountable through resolution.
• Build evaluation and deployment tooling, automation, recipes, and reference implementations to improve future engagements.
• Shape the product roadmap and ship fixes and features into Baseten’s codebase.
• Manage multiple accounts, sequence work, coordinate stakeholders, and communicate status and risk.

Requirements

• Minimum 1–2 years of software engineering experience shipping and maintaining code in large production systems, ideally across the stack.
• Experience debugging complex production issues using logs, metrics, and traces to identify root causes in unfamiliar systems.
• Ability to own ambiguous technical problems, triage issues, make decisions under uncertainty, and involve system owners when appropriate.
• Interest in working directly with customers and influencing product direction beyond pure engineering responsibilities.
• Ability to communicate complex technical topics with customer engineers, customer leadership, and internal stakeholders.
• Curiosity about AI inference and training and motivation to develop expertise in AI infrastructure.
• Willingness to respond to customers outside regular working hours and participate in an on-call rotation.
• Depth in infrastructure domains such as storage or networking, including InfiniBand or RoCE, is relevant.
• Experience operating distributed compute platforms such as Kubernetes, Slurm, or Ray, especially for GPU workloads, is relevant.
• Understanding of LLM architectures and inference engines such as vLLM, TensorRT-LLM, or SGLang is relevant.
• Ability to profile and optimize GPU workloads in training or serving is relevant.
• Hands-on experience with post-training techniques such as SFT and RL, or deep learning experience with PyTorch or JAX, is relevant.
• Operational experience with on-call work, incident response, and debugging distributed systems under pressure is relevant.

Benefits

• Competitive compensation including meaningful equity.
• 100% medical, dental, and vision insurance coverage for employees and dependents.
• Flexible PTO and a company-wide Winter Break, with offices closed from Christmas Eve through 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 and related learning and networking opportunities.