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Open nowPosted 9 hours ago

Staff SW Engineer

Fastino

Where
San Francisco, CA, USA
Work mode
On site
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Your applicationOpen nowStaff SW EngineerFastino · San Francisco, CA, USA

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Fastino's own form.

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The clock on this job

Early applications get read.

12.9% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 12.9%7 days
  4. 21.0%14 days
  5. 35.6%30 days
  6. 58.5%60 days
This job: posted 9 hours ago

The posting

Staff Software Engineer

Full-time | San Francisco

Introduction:

  • Join us at Fastino as we build the next generation of LLMs. Our team, boasting alumni from Google Research, Apple, Stanford, and Cambridge, is on a mission to develop specialized, efficient AI.
  • Fastino's GLiNER family of open source models has been downloaded more than 50 million times and is used by companies such as NVIDIA, Meta, and Airbnb
  • Fastino has raised $25M (as featured in TechCrunch) through our seed round and is backed by leading investors including Microsoft, Khosla Ventures, Insight Partners, GitHub CEO Thomas Dohmke, Docker CEO Scott Johnston, and others.

What You’ll Work On:

  • Help define the technical direction for Fastino’s platform, contributing to the architecture decisions that let us scale without slowing down.
  • Design and build our agent harness, including the runtime loop, tool calling, context management, and error recovery, plus the eval harnesses that measure agent reliability.
  • Build and scale our AI training and inference pipelines, pushing on latency, throughput, and cost as usage grows.
  • Own deployment of the Fastino platform into customer-managed AWS VPCs using Kubernetes and Terraform, including install, upgrade, and observability paths that work in environments we don’t control.
  • Build secure sandboxed execution environments for agentic workloads on Modal Sandboxes.
  • Ship full-stack features end-to-end while owning the hardest, most ambiguous problems on the team.
  • Partner closely with AI researchers to take new models from research to production, including deployment, evaluation, and monitoring.
  • Raise the engineering bar through code reviews, mentorship, and setting practices the rest of the team builds on, including how we work with AI coding agents.
  • Talk to customers and turn their feedback into product decisions, fast.

What We’re Looking For:

  • Required: 8+ years of hands-on software engineering experience, including time as a tech lead or driving architecture across a team or product.
  • Required: Strong architectural skills, with a track record of designing and scaling production systems that handle real load.
  • Required: Production experience with agentic systems, AI training pipelines, or inference pipelines, ideally more than one.
  • Required: Hands-on experience deploying software on AWS with Kubernetes and Terraform, ideally into customer-managed or multi-tenant environments.
  • Required: Experience building and running sandboxed execution environments for agent workloads, ideally with Modal Sandboxes.
  • Required: Extensive experience building with AI coding agents, and a demonstrated ability to guide them to produce clean, production-quality code. You know how to scope tasks, review output critically, and catch what agents get wrong.
  • Required: You thrive in ambiguity, make good calls without a playbook, and know when to move fast versus when to build for the long term.
  • Optional: Experience building agent harnesses or frameworks used in production, including familiarity with MCP or similar tool protocols.
  • Optional: Experience with LangSmith or similar tooling for tracing, evaluating, and monitoring agents.
  • Optional: Hands-on experience with inference optimization, like batching, quantization, GPU utilization, or serving frameworks such as vLLM, SGLang, or Triton.
  • Optional: Deep experience with high-concurrency backend systems or low-latency API design.
  • Optional: Startup experience, ideally as an early engineer helping a team grow.

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