Applied AI Engineer
3 weeks ago
The position ⚠️ Please read first This is a full-time, in-person role based in San Francisco (Presidio) - we work from the office 5 days a week . You must be based in the Bay Area or willing to relocate before starting . We require US work authorization , but are open to O-1 visa sponsorship for truly exceptional candidates. Roles & Responsibilities As an Applied AI Engineer , you'll be responsible for building, refining, and scaling the agent systems inside the company — from architecture to evals to deployment. This isn't a research role. We care about what works in production: fast response times, predictable behavior, traceability, and uptime. You'll work across the stack — with infra, frontend, and product — to make sure the agents users build inside the company are robust, useful, and usable. A few examples of what you might work on: Implement multi-step, tool-using agents that hit real APIs and handle retries, auth, timeouts, and edge cases. Build RAG pipelines that support grounded answers from structured and unstructured sources. Design agent memory systems that persist relevant state across runs — e.g., scratchpads, summary buffers, embedding stores. Add determinism + replay to agents so users can trace and debug behaviors step by step. Own and evolve our eval framework — both automated checks and human-in-the-loop scoring. ${your ideas}. Essential criteria 3 to 7 years minimum experience in AI in similar roles Previous work experience: either in an early-stage start-up (small team, high autonomy, fast pace)either in a recognized Big Tech company ( Meta, Google, AWS, etc. ) Fluent professional English (working language in San Francisco) Availability to work on-site in San Francisco, 5 days/week Opening of an O-1 visa (exceptional talent) – Company fully sponsors it Having already delivered complex products or contributed to open source projects The candidate 3+ years of engineering experience, including time shipping production software. Experience building and deploying agent-like systems — multi-step LLM pipelines, tool-using bots, scripted assistants, or similar. Hands-on experience with RAG pipelines, agent memory systems, tool use and orchestration, and evaluation. Ability to write production-grade code and work across systems without needing a spec. Thrives in fast-paced, product-first environments where the goal is shipping. Bonus: Experience with frameworks like LangChain, CrewAI, or DSPy, shipped agents live in the wild, familiarity with LLM ops, tracing, observability, and failure handling, and experience as a founder or early engineer. Technical skills Experience with multi-step LLM pipelines, tool-using bots, scripted assistants. Hands-on experience with RAG pipelines, agent memory systems, tool use and orchestration, and evaluation. Experience with frameworks like LangChain, CrewAI, or DSPy. Familiarity with LLM ops, tracing, observability, and failure handling. Recruitment process Application 15-min intro call: Quick check to align on location, motivation, and logistics. 45-minute technical interview: Build a small full-stack app. System design interview: Deep dive into how you think and architect systems. Final conversation: Quick vibe check, answer your questions, and scope out the work trial. Work trial: Paid, in-person, and real — typically 3 days to 2 weeks. Extras Full-time, in-person role based in San Francisco (Presidio) with work from the office 5 days a week. Open to O-1 visa sponsorship for truly exceptional candidates.
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