Software Engineer - MLOps

About Us

Phare Health is now part of R1 and its AI innovation engine, R37 Lab, bringing Phare’s frontier clinical reasoning technology together with one of the largest healthcare platforms in the U.S.

At R37 and Phare, we are building the first AI-native Healthcare Revenue Operating System: a connected platform that reasons over full medical records, payer logic, and financial workflows to automate medical coding, billing, and follow-up.

Backed by real customers, real data, and real distribution, we operate on a national scale. Our agentic AI systems already power production workflows across 95 of the top 100 U.S. health systems, processing hundreds of millions of patient encounters each year, including:

• 180M+ Claims
• 550M+ Patient encounters
• 1.2B+ Workflow actions and outcomes each year

This is startup-level ownership with enterprise-level impact. If you want to build AI that ships, scales, and measurably improves how healthcare works, this is the place to do it.

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 ML Ops 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.

Additionally, 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, IaaC (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)

Role Leveling

We are looking for candidates at various levels, ranging from Level 2 to Staff

• L2: Independently delivers a complete end-to-end project, owning design, implementation, and delivery of scoped work
• L3: Leads delivery of larger projects, handling increased technical complexity and ambiguity, providing light guidance to L2s on shared work
• Senior: Team Lead responsible for managing a portfolio of projects that contribute to major technical initiatives.
• Staff: Impact at the organizational level. Responsible for leading multiple teams or multiple broad initiatives simultaneously, ensuring that high-level technical goals are met across the entire organization.

Hiring Flow

• Intro call - your background & our mission alignment
• Technical deep-dives - pseudo-coding exercise and systems design (not Leetcode)
• Culture interview in person in NYC
• References
• Offer