Staff SW Engineer
Fastino Ai · San Francisco Bay Area
About this role
## Staff Software Engineer (Full-time) — San Francisco ### About Fastino - Build the next generation of LLMs at Fastino. - Team includes alumni from Google Research, Apple, Stanford, and Cambridge. - GLiNER open source models have been downloaded 50M+ times and are used by companies such as NVIDIA, Meta, and Airbnb. - Raised $25M (TechCrunch) backed by investors including Microsoft, Khosla Ventures, Insight Partners, GitHub CEO Thomas Dohmke, Docker CEO Scott Johnston, and others. ### What You’ll Work On - Define Fastino’s technical direction and contribute to architecture decisions that enable scaling without slowing down. - Design and build the agent harness: runtime loop, tool calling, context management, error recovery, and eval harnesses to measure agent reliability. - Build and scale AI training and inference pipelines, improving latency, throughput, and cost as usage grows. - Own deployment of the Fastino platform into customer-managed AWS VPCs using Kubernetes and Terraform (install/upgrade/observability in environments you don’t control). - Build secure, sandboxed execution environments for agentic workloads on Modal Sandboxes. - Ship full-stack features end-to-end, owning the hardest and most ambiguous problems. - Partner with AI researchers to take new models from research to production (deployment, evaluation, monitoring). - Raise the engineering bar via code reviews, mentorship, and team practices—especially how you work with AI coding agents. - Talk to customers and turn feedback into product decisions quickly. ### 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/product. - Strong architectural skills and a track record of designing/scaling production systems handling real load. - Production experience with agentic systems, AI training pipelines, or inference pipelines (ideally more than one). - Hands-on AWS deployment experience with Kubernetes and Terraform, ideally in customer-managed or multi-tenant environments. - Experience building and running sandboxed execution environments for agent workloads, ideally with Modal Sandboxes. - Extensive experience building with AI coding agents; ability to guide them to produce clean, production-quality code. - Ability to scope tasks, critically review outputs, and catch agent mistakes. - Thrives in ambiguity; makes good calls without a playbook; knows when to move fast vs. build long-term. **Optional** - Experience building agent harnesses/frameworks used in production; familiarity with MCP or similar tool protocols. - Experience with LangSmith or similar tooling for tracing, evaluating, and monitoring agents. - Inference optimization experience (batching, quantization, GPU utilization, serving frameworks like vLLM, SGLang, Triton). - Deep experience with high-concurrency backend systems or low-latency API design. - Startup experience, ideally as an early engineer helping a team grow.
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