Staff ML Systems Engineer - Explainability

About the Role: This role owns the explainability stack that institutions will base deployment decisions on. You'll build SHAP overlays that don't just run in notebooks - they run in production, under 100ms, with real trace logs, tied to real decisions. It's fast, it's visible, and it has to hold up in court. What You'll Build 1. SHAP + Retrieval Explainability Build attribution for both retrieval and generation paths Design SHAP-based systems that can run inline with model responses Handle vector search + hallucination tracing, not just token salience 2. Infrastructure for Real Users Integrate explainers across Claude, GPT-4, Mistral - with version control and adapter logic Build APIs that feed into dashboards, audit logs, and human-readable traces No toy demos. Every output has to stand up to legal review 3. Enforcement Hooks Align your outputs with EU AI Act Article 10 / 14 requirements Feed explainability directly into policy engines, risk graphs, and compliance frontends Help product and sales use your stack as a reason to buy, not a nice-to-have

Who You Are: You've shipped ML infra - real-world scale, real latency targets You know the limits of SHAP and how to work around them You don't wait for product specs - you work from first principles

Bonus: You've had to explain a model to someone who doesn't trust it (a lawyer, a doctor, an auditor) Why It Matters Every buyer we talk to - from governments to hospitals - wants explainability. But most of what's out there doesn't scale or doesn't mean anything to the people making the actual decisions. This role builds the version that matters. You won't just make models legible - you'll help make them safe to use. Logistics & Application

Start Date: Flexible (Q3 ideal)

Location: Remote-first, timezone overlap with NY or EU preferred Comp: Top of market salary + real equity