ML Ops Engineer: Production AI for Healthcare (Hybrid)

R1 RCM Holdco Inc.; New York (NY); Hybrid; USD 140,000 - 270,000; Full time

Benefits offered by this job

Top-of-market compensation with bonus

Flexible PTO

Comprehensive health benefits

401(k) matching

Job summary

R1 RCM Inc. is seeking a Software Engineer for the ML Ops team in a hybrid role based in New York City. You will own the production runtime for Phare's ML stack, deploying, serving, and scaling models across inference endpoints and batch workflows.

You will build progressive delivery pipelines, manage latency and availability SLAs, and instrument end-to-end observability. The role emphasizes Terraform, Kubernetes, and CI/CD to ensure reproducible ML releases at scale.

Qualifications

• Experience deploying and operating models in production on GPUs.
• Strong with Docker/Kubernetes, IaC (Terraform) and CI/CD for services and model artifacts.
• End-to-end observability (metrics, logs, traces, drift) and incident response.
• RBAC, secrets management, encryption and audit logs for post-training lifecycles.

Responsibilities

• Own production runtime for Phare's ML stack; deploy, serve, and scale models across 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, model telemetry) and implement alerting and runbooks.
• Harden platform with Terraform, Kubernetes, and CI/CD for reproducible ML releases.

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 in a hybrid role based in New York City. You will own the production runtime for Phare's ML stack, deploying, serving, and scaling models across inference endpoints and batch workflows.

You will build progressive delivery pipelines, manage latency and availability SLAs, and instrument end-to-end observability. The role emphasizes Terraform, Kubernetes, and CI/CD to ensure reproducible ML releases at scale.