Member of Technical Staff
About the role
At Simplify, we work directly with startup founders to help them hire for high-priority roles. We're partnering with a fast-growing AI inference company in San Francisco to hire Members of Technical Staff — engineers who build the systems that make LLM inference fast, and own the customers running on them.
About the company
Building the infrastructure that makes LLM inference fast — and putting the engineers who build it directly in front of the customers who run on it. Small team, HQ in San Francisco. Their customers include Vercel and Neon Health, and the engineers own those relationships directly — no solutions team, no layer between you and the workload.
Most infra engineers ship into a backlog and never meet the people running their code. Here, sales gets the first meeting — from there, you decide what to prove, build it, win the technical evaluation on the customer's own workload, and keep it running in production. Each engineer owns 3–4 named accounts outright.
Responsibilities
- Develop and optimize high-performance computing kernels, and work across inference engine internals and serving infrastructure
- Develop AI agents to do autonomous inference engineering
- Design, deploy, and operate heterogeneous clusters across vendors
- Own customer accounts end to end. Sales gets the first meeting. From there you decide what to prove, you build it, you keep it running in production, and you carry the relationship
- Win the technical evaluation. Prove Wafer on the customer's own workload rather than a synthetic benchmark, and be the person who can explain the result to their engineers
- Own production for your accounts. When latency moves or error rates climb, you find it, you fix it or route it, and you are who the customer hears from
- Inform product roadmap. You sit closer than anyone to how Wafer behaves under real load, and the engineering team builds against what you report
Requirements
- You've shipped and operated production systems — been on call, personally debugged production problems, and can walk through how you found them
- You've owned a customer relationship or a deployment with real users — you can name it, and you were the person people came to
- Credible in a room full of engineers — you can defend a benchmark methodology
- Able to work without a spec
- In person in San Francisco, 5 days/week
- Already in the US (they sponsor and transfer essentially any visa status — H-1B, O-1, TN, STEM OPT)
Nice to have
- Inference / model serving experience (vLLM, SGLang, TensorRT-LLM, Triton) — their most useful differentiator, but they'll interview a strong engineer without it
- Early-stage or founding experience — if you've sold your own product to engineers, you've already done both halves of this job
- Heavy use of AI agents and tooling in your own workflow
Benefits
- $200–300K base + equity
- $1K/month housing stipend near the office
- Covered Uber/Waymo rides
- No years-of-experience requirement in either direction — the bar is what you've operated, not how long you've worked
- Your work shows up directly in customer production systems, and you carry the relationship
- Small fast-moving team, high ownership — you own named accounts outright from day one