Own the production runtime for Phare’s ML stack
Deploy, serve, and scale models across inference endpoints and batch/streaming workflows
Build progressive delivery pipelines with automated rollouts and rollbacks
Manage SLOs for latency and availability
Instrument end-to-end observability (metrics, logs, traces, drift, regression)
Harden the platform with Terraform, Kubernetes, and CI/CD

Requirements

5 years of software engineering experience
2 years of ML Ops experience
Production ML: deployed and operated models running on GPUs
Strong with Docker/Kubernetes, IaaC (e.g., Terraform), and CI/CD
System Reliability: progressive delivery with automated rollouts/rollbacks
Manage model registries and stage gates, design retraining and enforce RBAC

Benefits

Top-of-market compensation (salary + equity)
Flexible PTO
Hybrid in-office (min. 3 days per week)
Comprehensive health benefits
401(k) matching
Inspiring, brilliant, mission-driven teammates

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningmodel deploymentmodel servingmodel scalingprogressive deliveryautomated rolloutsautomated rollbacksobservabilityinfrastructure as codesystem reliability