Remote ML Ops Engineer for Health AI Platform

R1 RCM, Inc.; Boise (ID); 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

Inspiring teammates

Job summary

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

You will build progressive delivery pipelines with automated rollouts and rollbacks, while managing SLOs for latency and availability. You will harden the platform with Terraform, Kubernetes, and CI/CD, ensuring reproducible, auditable ML releases and robust observability.

Qualifications

• Deployed and operated models in production (GPUs) for APIs and batch/streaming inference.
• Maintains environment parity, reproducible releases and data lineage for models.
• Implements end-to-end observability and automated rollout/rollback using progressive delivery.
• Manages model registries, stage gates, retraining and RBAC/secrets/audit logs.

Responsibilities

• Own production runtime for Phare’s ML stack and scale models across endpoints and workflows.
• Build progressive delivery pipelines with automated rollouts/rollbacks and instrument observability.
• Harden platform with Terraform, Kubernetes, and CI/CD for auditable ML releases.
• Ensure uptime and feedback loops are integrated into ML systems.

Skills

Production ML

Platform engineering

System Reliability

Post-training lifecycles

Tools

Docker

Kubernetes

Terraform

CI/CD

Job description

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

You will build progressive delivery pipelines with automated rollouts and rollbacks, while managing SLOs for latency and availability. You will harden the platform with Terraform, Kubernetes, and CI/CD, ensuring reproducible, auditable ML releases and robust observability.