Software Engineer - ML Ops; R37

Remote, USA

R37

Full time

R

At R1, we’re transforming how healthcare works by combining nearly two decades of revenue cycle expertise with advanced technology, including analytics, AI, intelligent automation and workflow orchestration. We pair that technology with the irreplaceable expertise of our people to help hospitals, health systems and medical groups improve financial performance, simplify administrative complexity and create better experiences for patients and providers.

All Together Better defines our culture and the way we work. We’re a diverse, global team united by a shared mission to make healthcare work better for all. Across technology, operations and support functions, our people bring deep expertise, empathy and ingenuity to the work we do. Here, collaboration fuels progress, adaptability sparks innovation and every voice can help shape what’s next for R1 and the future of healthcare.

R37 is R1’s AI innovation lab, building technology to transform healthcare.

Our flagship platform, Phare OS, uses AI and automation to improve critical pre-bill workflows, including authorization, utilization management, documentation and coding. By identifying and resolving issues before claims are submitted, Phare OS helps prevent denials, improve reimbursement and move revenue cycle work upstream.

What makes R37 different is the opportunity to build advanced AI at real-world scale. R1 works across 95 of the top 100 U.S. health systems, giving R37 access to the data, workflows and reach needed to make an immediate impact:

• 180M+ claims
• 550M+ patient encounters
• 1.2B+ workflow actions and outcomes annually

This is startup-level ownership with enterprise-level impact. You’ll build technology that moves from idea to real healthcare environments, solving complex problems at scale and changing how healthcare works.

As a Software Engineer for the ML Ops team, you will help own the production runtime for Phare’s ML stack - deploying, serving, and scaling models across inference endpoints and batch/streaming workflows.

Every day you will 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.

To thrive in this role, you must have a background in operating ML systems at scale, where uptime and feedback loops matter as much as accuracy.

We prefer this role to be Hybrid (3 days onsite) at our SOHO Office Hub in New York City.

• 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 life cycles :
  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 if you have:
Experience in regulated environments (e.g., Healthcare, Finance)

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 (Compensation Range for L2: $140k - $200k)
• L3…