# Forward Deployed Engineer

**Company:** [Baseten](https://hotfix.jobs/companies/baseten)
**Location:** San Francisco, CA, New York, NY
**Role:** Solutions Architecture
**Salary:** $200k – $400k/yr
**Experience:** 1+ years
**Skills:** Python, Kubernetes, Slurm, Ray, Gpu Computing, Llm Architectures, vLLM, Tensorrt-Llm, Sglang, PyTorch, JAX, InfiniBand, Roce, Reinforcement Learning, Distributed Systems
**Posted:** 2026-08-19

> Forward Deployed Engineers own technical outcomes for major AI customers, taking workloads from ambiguous objectives through production while optimizing inference, post-training, and infrastructure. The role requires production software engineering, complex debugging, customer communication, and operational ownership.

## Job Description

## Responsibilities
- Act as the de facto CTO for customer accounts on Baseten, owning how workloads are designed, operated, and scaled.
- Translate customer objectives into specifications, proofs of concept, production systems, and success criteria.
- Design evaluations and benchmarks, diagnose quality or performance gaps, and improve inference, post-training, or evaluation workflows.
- Respond to mission-critical failures, lead triage and incident resolution, and remain accountable through deployment of fixes.
- Build tooling, automation, recipes, and reference implementations for evaluation and deployment infrastructure.
- Influence the product roadmap and ship fixes and features into Baseten's codebase.
- Manage multiple accounts, prioritize work, and align customers and internal stakeholders on status and risk.

## Requirements
- 1–2 years of software engineering experience shipping and maintaining code in large production systems.
- Experience debugging complex production issues using logs, metrics, and traces.
- Ability to own ambiguous technical problems, make decisions under uncertainty, and collaborate with system owners.
- Interest in working directly with customers and influencing product development.
- Strong communication skills with both technical and executive audiences.
- Curiosity about AI inference and training and infrastructure supporting it.
- Willingness to respond outside regular working hours and participate in an on-call rotation.

## Nice-to-haves
- Depth in infrastructure domains such as storage systems or networking, including InfiniBand or RoCE.
- Experience operating distributed compute platforms such as Kubernetes, Slurm, or Ray, particularly for GPU workloads.
- Understanding of LLM architectures and inference engines such as vLLM, TensorRT-LLM, or SGLang.
- Experience profiling and optimizing GPU workloads for training or serving.
- Experience with post-training techniques such as SFT and RL.
- Deep learning experience and fluency with PyTorch or JAX.
- Operational experience with on-call, incident response, and distributed-systems debugging.

## Compensation & Benefits
- Competitive compensation, including meaningful equity.
- Medical, dental, and vision insurance fully covered for employees and dependents.
- Flexible paid time off and company-wide winter break.
- Paid parental leave.
- Fertility and family-building stipend through Carrot.
- Company-facilitated 401(k).
- Exposure to a variety of machine-learning startups.

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