Senior AI Engineer

We're building the kind of platform we always wanted to use: fast, flexible, and built for making sense of real-world complexity. Behind the scenes is a robust, event-driven architecture that connects systems, abstracts messy workflows, and leaves room for smart automation. The surface is clean and simple. The interactions are seamless and intuitive. The machinery underneath is anything but. That’s where you come in.

We’re a well-networked founding team with strong execution roots and a clear roadmap. We’re backed, focused, and delivering fast.

We're looking for a Senior AI Engineer to own our AI program end-to-end. Not a prompt engineer. Not a data engineer. The person who owns how our models get selected, trained, tuned, routed, and evaluated — and who walks in with the confidence to define the architecture from the hardware up. You'll work directly with the CTO, lead our fine-tuning strategy (LoRA is going to be core for us), and decide how we get the most out of our GPU spend. This is a senior IC role in a flat org — no management required, but you'll be the technical anchor other engineers learn from.

• Own our fine-tuning strategy end-to-end — LoRA first, full fine-tunes where they earn it. What we tune, on what hardware, against what evals
• Define the GPU architecture. We have working infrastructure (H100 for production, A100 for training); your call to confirm, reshape, or rebuild
• Drive model selection and routing across Gemini, Anthropic, and OpenAI — the right model for the right job, with cost and latency in the equation
• Build agentic LLM pipelines using LangChain, LangGraph, and LangSmith
• Design and iterate on prompt strategies, with a focus on consistency and context
• Construct retrieval-augmented generation (RAG) systems from scratch
• Instrument evaluation metrics, telemetry, and feedback loops to guide model and prompt evolution
• Work alongside product, frontend, and backend engineers to tightly integrate AI into user-facing flows

• Python (primary language for all ML, LLM, and orchestration work)
• LangChain + LangGraph + LangSmith
• Gemini / Anthropic / OpenAI model routing logic
• H100 targets for production inference, A100 for training (open to your redesign)
• Postgres, and custom orchestration via MCP
• GitHub Actions, GCP

There’s enough here to move fast, but still plenty of room for your fingerprints.

• Engineers come first: your time, focus, and judgment are respected
• Deep work > chaos: fixed cycles & cooldowns protect focus and keep context switching low
• Autonomy is the default: trusted builders who own outcomes, no babysitters
• Ship daily, safely: merge early, integrate vertically, ship often, use feature flags, and keep momentum
• Outcomes over optics: solve real problems, not ticket soup
• Voice matters: from week one, contribute, improve something, and shape how we build
• Senior peers, no ego: collaborate in a high-trust, async-friendly environment
• Bold problems, cool tech: work on complex challenges that actually move the needle
• Fun is part of it: we move fast, but we also celebrate wins and laugh together

• 8+ years building production-grade ML, data, or AI systems
• Hands-on experience training and fine-tuning models — LoRA, QLoRA, adapter methods, or full fine-tunes. Actual model work, not just prompt iteration
• Confidence to define GPU architecture given a goal and a budget — hardware choices, training strategy, cost/performance tradeoffs
• Strong grasp of prompt engineering, context construction, and retrieval design
• Comfortable working in LangChain and building agents, not just chains
• Strong Python: testable, maintainable, clearly structured
• Understanding of model evaluation, observability, and feedback loops
• Excited to push from prototype → production → iteration
• Senior IC judgment: you scope your own work, push back when it's right, and make calls others can build on
• Confident English skills to collaborate clearly and effectively with teammates

• Have shipped a fine-tuned model into production and can walk us through the tradeoffs you made
• Have built agent-like workflows with LangGraph or similar
• Have worked on semantic chunking, vector search, or hybrid retrieval strategies
• Can walk us through a real-world model or prompt failure — and how you fixed it
• Have experience with PySpark, Databricks, or lakehouse architecture
• Have contributed to OSS tools or internal AI platforms
• Think of yourself as both an engineer and a systems designer
• Have mentored other senior engineers and enjoyed it

• Must be based in San Francisco, Las Vegas, or Tel Aviv
• Full-time role with competitive comp
• Flexible hours, async-friendly culture, engineering-led environment