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Member of Technical Staff, Forward Deployed

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Pay not postedSan Francisco, CAOn-site

Own customer deployments from workload benchmark through production, then monitor and improve their ongoing performance.

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At a glance

Posted base pay

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The work

Builds what the customer runs

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The job description from Wafer

Posted by Wafer on

Member of Technical Staff, Forward Deployed

At Wafer, we are building AI systems that automatically optimize inference workloads across silicon. Wafer is well funded and serves trillions of tokens a month for mission-critical workloads at fast-growing AI startups.

Forward deployed Members of Technical Staff are the engineers our customers work with. You'll take each customer from first benchmark to production, and keep their workloads fast and healthy after that. Our performance engineers and optimization agents do most of the tuning. You make sure it lands for each customer and stays that way.

This is its own track, with the same bar as the rest of our technical staff. Sales finds customers and closes them. You are the engineer beside them from the first benchmark onward, not a salesperson.

What you'll do

  • Run the technical evaluation on the customer's own workload: understand their traffic and latency targets, run the benchmark and explain the result to their engineers.
  • Get each customer into production on Wafer, working with our performance engineers and agents to hit their targets.
  • Own the health of your accounts. Watch latency, errors and usage, catch problems before the customer does, and be who they hear from.
  • When a problem needs a fix in the engine, the kernels or the agents, scope it and bring the right engineer everything they need to solve it.
  • Build the tools, integrations and runbooks that make each new deployment faster than the last.
  • Tell engineering and sales what customers need next, based on what you see in production.

What we look for

  • You are a strong engineer who has shipped and operated production systems. You can read a trace, open a profiler and find the problem yourself.
  • Experience with LLM inference or serving, such as vLLM, SGLang or TensorRT-LLM, is a big plus.
  • You want to work with customers. You have owned a technical customer relationship and can explain results to a skeptical engineering team.
  • You are credible in a room full of engineers. You can defend a benchmark methodology and concede the point when they are right.
  • You work without a spec. The problem arrives half-defined and you come back with a scoped answer rather than a list of questions.

How we evaluate

We score every candidate on seven values:

  1. Infinitely Resourceful
  2. Exceptionalism
  3. Unreasonable Standards
  4. Company Over Self
  5. High EQ
  6. Learns Quickly
  7. First Principles Thinker

Benefits

  • Fully covered medical, dental, and vision insurance.
  • Daily lunch and dinner, unlimited PTO, and parental leave.
  • $1K/month housing stipend (post-tax) if you live within walking distance (0.5 miles) from the office.
  • Relocation allowance and benefits.
  • Covered Uber/Waymo from/to office.
  • Visa sponsorship available.

How we work

On-site in San Francisco, five days a week. Small team with massive surface area and ownership. You operate with complete autonomy of how to solve problems.

Your success is measured by customers who move their production traffic to Wafer and stay because it keeps getting faster.

View posting at Wafer

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About Wafer

Continuously optimizes and serves AI inference workloads across models, kernels, serving engines, and accelerator hardware.

Series A · 10–50 people

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Conditions

Location
San Francisco, CA
Work arrangement
On-site
Employment type
Full-time
Visa sponsorship
Available