Distinguished Agent Engineer
About the role
Distinguished Agent Engineer
Full-time | San Francisco
About Fastino
We're building specialized, efficient language models that beat big general models on the tasks that matter. Our team includes alumni from Google Research, Apple, Stanford, and Cambridge.
Our open source GLiNER models have been downloaded 50M+ times and are used by teams at NVIDIA, Meta, and Airbnb.
We've raised $25M (covered in TechCrunch) from Microsoft, Khosla Ventures, Insight Partners, GitHub CEO Thomas Dohmke, Docker CEO Scott Johnston, and others.
The Role
This is one of our most senior IC engineering role. You'll set the technical direction for how agents get built on Fastino models, write a lot of the core code yourself, and raise the bar for everyone around you. You've built agentic systems that real users depend on, and you have strong opinions about what works.
What You'll Work On
Design and own the architecture for agentic systems built on Fastino's small, fast models: planning, tool use, memory, and multi-agent orchestration.
Figure out where specialized models beat frontier LLMs inside agent loops, and prove it with benchmarks and real customer workloads.
Build the evals, telemetry, and feedback loops that tell us when agents are getting better (or worse) and why.
Partner with research to turn new model architectures into agent capabilities, and shape what research works on next.
Make agents fast and reliable at scale: low latency inference, high concurrency serving, graceful failure handling.
Drive data and fine-tuning strategy for agent specific tasks like tool calling, extraction, and routing.
What We're Looking For
5+ years of software engineering experience, with 3+ years building with LLMs or ML in production.
A track record of shipping agentic systems that real users rely on, and the scars to prove it.
Deep experience across the stack: prompt and context engineering, tool and API design, vector DBs, Kubernetes, and inference serving.
Strong understanding of model behavior: fine-tuning, evals, failure modes, and latency/cost tradeoffs.
Experience setting technical direction across teams, not just within one project.
You write clearly and make complicated systems feel simple.
Bonus: experience building high concurrency microservices for agent workloads.
Bonus: familiarity with GLiNER or other information extraction architectures.
Bonus: open source contributions, papers, or talks on agents or LLM systems.