Software Engineer - MLOps

About Phare & R1

Phare is building healthcare's first Revenue Operating System - a platform that uses AI to make hospital billing and reimbursement effortless, accurate, and fair. We recently became part of R1, one of the largest companies managing healthcare claims which serves hundreds of systems around the country. This gives us the best of both worlds : the DNA of an AI startup paired with the scale of a healthcare organization that supports care delivery for hundreds of healthcare systems nationwide. Join us on our mission to build a fairer, faster model for healthcare payments.

The Role

You'll own the production runtime for Phare's ML stack - deploying, serving, and scaling models across inference endpoints and batch / streaming workflows. You'll build progressive delivery pipelines with automated rollouts and rollbacks, manage SLOs for latency and availability, and instrument end-to-end observability (metrics, logs, traces, drift, regression). You'll harden the platform with Terraform, Kubernetes, and CI / CD, ensuring reproducible, auditable ML releases.

We are hiring across several seniority levels ranging from Mid-level up to Staff. At a minimum, we expect 5 years of software engineering experience with 2 years of MLOps experience.

This is an in-person role in NYC requiring at least 3 days in the SoHo office.

About you

You have a background in operating ML systems at scale, where uptime and feedback loops matter as much as accuracy. You have experience in :

• Production ML : You've deployed and operated models running on GPUs in production - APIs and batch / streaming inference.
• Platform engineering : Strong with Docker / Kubernetes, IaC (e.g., Terraform), and CI / CD for services and model artifacts; you maintain environment parity, reproducible releases, and robust model / experiment versioning with data lineage.
• System Reliability : You use progressive delivery with automated rollouts / rollbacks, and you build end-to-end observability (metrics, logs, traces, and model telemetry for drift / regression) plus actionable alerting, runbooks, and incident response.
• Post-training lifecycles : You manage model registries and stage gates, design scheduled or event-driven retraining when appropriate, and enforce RBAC, secrets management, encryption, and audit logs.
• Bonus : Experience in regulated environments (e.g., healthcare, finance)

Benefits

Hiring Flow