Principal Engineer – Distributed Systems (GPU Edge + Inference)

Full-time | Remote | Infrastructure | Reports to CTO About Elloe Elloe is the trust layer for AI. We sit between the world’s most powerful language models and the institutions that can't afford to get it wrong — hospitals, banks, regulators. We trace and block failures in real time. That’s not marketing — we’re deployed at the European Commission, with NIH clinical trials, and inside a Top-5 EU bank catching GDPR violations live. This is the enforcement layer GenAI has been missing. We're not visualizing problems — we're fixing them. About the Role You’ll lead our GPU-edge inference systems. From chaos-resilient deployment to SHAP-driven compliance metrics, you’ll own global infra that makes AI safe and performant. What You’ll Own 1. Global Edge Routing Design zone-routing that ensures <50ms SLA in 10+ regions Build fallback orchestration to handle compliance-aware rollbacks 2. GPU Infra Ops Maximize utilization across 100K+ GPUs via mesh & load prediction Integrate compliance overlays with VaultChain and SHAP triggers 3. Reliability Telemetry Ship `/vault/audit`, `/inference/predict`, `/compliance/log` endpoints Trace every edge request across governance and model layers Who You Are Senior systems engineer with GPU fleet experience (KubeRay, Istio, Envoy) Operated real-time AI infra with 10M+ QPS loads Comfortable with compliance observability and infra governance Why This Matters Our competitive edge isn’t just AI — it’s defensible enforcement. This role turns that into product. You’ll Leave This Role With Referenceable contributions to enforcement infra that’s live in EU and US institutions First-hand product work across legal, engineering, and GTM teams Influence over how regulatory primitives become systems people trust Logistics & Application Start Date: Flexible (Q3–Q4 ideal) Location: Remote-first; timezone overlap with NY or EU preferred Compensation: Top of market salary + equity To Apply: Send your resume and a sentence on the hardest infra problem you'd want to own at scale.