ML Ops Engineer - Production ML for Healthcare (Remote)

R1 RCM, Inc.; Salt Lake City (UT); Hybrid; USD 140,000 - 270,000; Full time

Benefits offered by this job

Top-of-market compensation

Flexible PTO

Comprehensive health benefits

401(k) matching

Mission-driven teammates

Job summary

R1 RCM, Inc. is seeking a Software Engineer for the ML Ops team to own the production runtime for Phare’s ML stack, deploying, serving and scaling models across inference endpoints and batch/streaming workflows.

You will build progressive delivery pipelines, manage SLOs, and instrument end-to-end observability. The role requires operating ML systems at scale; hybrid work with 3 days onsite at the New York City SOHO Office Hub.

Qualifications

• Production ML: deployed and operated models running on GPUs in production—APIs and batch/streaming inference.
• Platform engineering: strong with Docker/Kubernetes, IaaC (Terraform), and CI/CD for services and model artifacts; environment parity and release versioning.
• System Reliability: progressive delivery with automated rollouts/rollbacks; end-to-end observability and alerting.
• Post-training lifecycles: manage model registries, gates, retraining, RBAC, secrets, encryption, audit logs.

Responsibilities

• Own the production runtime for Phare’s ML stack—deploying, serving, and scaling models.
• Build progressive delivery pipelines with automated rollouts and rollbacks.
• Manage SLOs for latency and availability; instrument observability (metrics, logs, traces, drift).
• Harden platform with Terraform, Kubernetes, and CI/CD for reproducible ML releases.

Skills

Production ML

Platform engineering

System reliability

Post-training lifecycle

Tools

Docker

Kubernetes

Terraform

CI/CD

Job description

R1 RCM, Inc. is seeking a Software Engineer for the ML Ops team to own the production runtime for Phare’s ML stack, deploying, serving and scaling models across inference endpoints and batch/streaming workflows.

You will build progressive delivery pipelines, manage SLOs, and instrument end-to-end observability. The role requires operating ML systems at scale; hybrid work with 3 days onsite at the New York City SOHO Office Hub.