Description review
Distinguished Agent Engineer
Fastino · Remote - San Francisco Bay Area · back to the listing
HR standards
52/100
needs work
Title ↔ description
74/100
solid
Reads as
Machine Learning Engineer
93% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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Startups
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What the listing never says
- 18 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No section describes what the person would actually do. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
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.
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.