[{"@context":"https://schema.org","@type":"JobPosting","title":"GTM","description":"

About the team

Wafer runs dedicated and serverless inference for open-weights models. We use AI to optimize AI infrastructure: we tune the whole serving stack so the same model runs faster on cheaper hardware, and our customers keep the difference. We are seven people in San Francisco and plan to double that as fast as we can hire.

About the role

We are hiring our first dedicated go-to-market hire to own the top of the funnel.

Your job is to create qualified first meetings with AI-native companies running production inference.

For the right person this is a fast way into technical sales. The cycle is short and you will be in conversations with CTOs in your first month. We would rather promote you than hire over you.

This role is in person in San Francisco, five days a week.

In this role, you will:

Own outbound volume. Roughly 80+ touches a day across email, LinkedIn, cold calls, and whatever else gets a reply.
Book qualified first meetings that convert into benchmarks. A meeting that was never going to benchmark is worse than no meeting.
Build and test multi-step sequences, and report reply and meeting rates every week with real numbers.
Work with the closer and forward deployed engineer before every first meeting so nobody walks in cold.
Keep the CRM up to date.

You might thrive in this role if you:

Have at 12+ months of B2B outbound ai a high-growth AI-native company, activ","datePosted":"2026-07-27","employmentType":"FULL_TIME","hiringOrganization":{"@type":"Organization","name":"Wafer","sameAs":"https://www.wafer.ai"},"url":"https://jobs.ashbyhq.com/wafer/6fdf1862-52e9-40f3-9512-d9e11b3b4078","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}}},{"@context":"https://schema.org","@type":"JobPosting","title":"Forward Deployed Engineer","description":"
Forward Deployed Engineer
Inference performance depends on how efficiently models use the underlying hardware. Techniques across kernels, compilers, runtimes, and serving systems can dramatically improve latency and throughput. None of that matters until it is running in a customer's production traffic.
At Wafer, we are building AI systems that automatically optimize inference workloads across silicon. The goal is fungible token capacity. Any accelerator optimized toward serving inference most efficiently.
Wafer is well funded and serves trillions of tokens a day for mission critical workloads. We serve the highest performance inference to fast-growing AI startups.
Forward Deployed Engineers serve as the link between Wafer's core product and customers' challenges, managing the relationship from start to finish.

What you'll do
- Own customer accounts end to end. Sales gets the first meeting. From there you are both the customer's engineer and their point of contact: 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.
- Ship the integration. Stand up dedicated deployments and optimize the full stack for the customer's workload.
- 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.
- Member of Technical Staff
  Our mission at Wafer is to maximize intelligence per watt by using AI to optimize AI infrastructure, achieving orders of magnitude better energy and cost efficiency per token.
  We believe cheap intelligence is the most essential piece of technology for a future of abundance. We care about building a future where intelligence is \"too cheap to meter.\"
  Wafer commercializes these efforts by serving serverless and dedicated inference for open source LLMs at the best performance per dollar. Our core bet is doing this through autonomous optimization of heterogeneous hardware.
  What you'll do
  - Ship day-zero support for new open-source models, tuned for latency and throughput
  - Optimize the serving stack: batching, KV cache, speculative decoding, quantization
  - Write and tune kernels in CUDA, HIP, and Triton for NVIDIA, AMD, TPU, Trainium, D-Matrix, and more.
  - Design, deploy, and operate heterogeneous clusters across vendors
  - Run production inference across a mixed fleet: reliability, observability, and cost per token at scale
  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

About the team

About the role

In this role, you will:

You might thrive in this role if you:

Forward Deployed Engineer

What you'll do

What you'll do

How we evaluate

Wafer

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