Applied AI / ML Engineer
Full-time · Remote-hybrid (US / EU overlap)
Why we need this
Models are rarely the whole product. We need people who can pair retrieval, evaluation, and guardrails with honest latency and cost budgets.
With customers (expected)
- Explain model limits, data needs, and failure modes to non-ML stakeholders before they commit in a meeting.
- Join sprint reviews; capture client feedback on false positives/negatives and turn it into backlog, not defensiveness.
- Co-write the “what we measured and what we won’t claim” section of readouts so trust scales.
Responsibilities
- Design and ship ML features inside end-to-end slices—not notebooks that stop at accuracy.
- Own eval harnesses, regression tests, and failure modes for production-ish loads.
- Collaborate with integration engineers on APIs, batch vs stream, and human-in-the-loop flows.
You might be a fit if
- You reach for baselines before transformers.
- You’ve shipped something users actually hit, not only Kaggle-shaped data.
- You write the doc you wish you’d had six months ago—and you’re willing to read it aloud to a client.
Nice to have
- · Industrial or regulated-domain experience
- · Edge inference experience
- · Open-source contributions