Member of Technical Staff, Applied AI

Own the engine that turns every investigation into a compounding asset: the more the platform runs, the sharper it gets. This is applied ML systems, not research. If your goal is training foundation models from scratch and publishing, this is not the seat.

A note on location

Remote, with four hours of overlap with Pacific or IST. We don't count hours. We ask for the overlap because this work involves a lot of come look at this eval result with me, and that falls apart across a twelve-hour gap.

If you're in Seattle, Toronto or Bengaluru, we'd like to see you in person sometimes. It isn't a condition of the job.

The bar

• 10+ years of relevant engineering experience.
• Production ML systems experience end to end: data pipelines, fine-tuning, eval, deployment.
• Hands-on fine-tuning and distillation with open-weight models (Qwen, Llama-class).
• Eval harness design for correctness-sensitive tasks, including trajectory-level evals for agentic and tool-use systems.
• Inference deployment and scaling (vLLM or equivalent) on managed platforms.
• Proficient in day-to-day work with CLI-based AI coding tools like Claude Code (or an equivalent). It's how the team operates, not a nice-to-have.

What you'll do

• Instrument the product so every investigation, especially human-corrected ones, becomes structured training data.
• Build the eval harness for diagnostic accuracy. Root-cause correctness has to be measured, not eyeballed.
• Distill expensive deep-investigation runs into small, fine-tuned models that hold the quality bar at a fraction of the cost.
• Deploy inference, including on-prem and airgapped SLMs for customers whose logs cannot leave their environment.

Bonus

• Agent or tool-use systems.
• Preference optimization (DPO/GRPO-style) and structured-output / function-calling fine-tunes.
• On-prem or airgapped model deployment.
• Retrieval over large heterogeneous corpora.
• Systems or infra background.

What we offer you

• A front seat as we scale. Customer calls, the roadmap, the pipeline, how the company is actually doing. What you build carries your name on it, and the scope grows faster than the org chart does.
• Top-of-market salary, and founding equity worth having. You're early enough that the ownership is real, not decorative. We'll walk through the full picture on the first call, and this shouldn't be a guessing game.
• AI, all the way down. We use it for everything: writing code, reviewing it, research, ops, internal tooling. Any tool that unblocks you, we buy. If you already work this way, you'll feel at home in a day.
• Your calls to make, from week one. Architecture, tooling, how the work gets done. These aren't consultations here.
• Support while you make them. The founders are hands-on. You own the decision but you don't make it alone.
• Comprehensive medical insurance for you and your family.
• Start-up perks and flexible time off, the kind people actually take.
• A lean team, senior, and kind in a way that's rare at this pace. People share what they know and help before they're asked. It's the part of this job we're proudest of.

What to expect after you apply

• Screening call with Head of People & Business Operations
• Technical conversation with Co-founder/ Head of Engineering
• Technical exercise with Co-founder/ Head of Engineering
• Final conversation with CEO
• References, then offer

In your application, tell us what you'd own here, and the hardest thing you've shipped in this domain.