ML Engineer -RCM (Interface APIs)

Role Overview

We are hiring an ML Systems Engineer to own the production interface layer between our denial prediction models and real-world RCM workflows. This role is responsible for delivering lowlatency, highly reliable inference APIs, integrating AI predictions into claims scrubbing and billing workflows, and ensuring real-time decisioning at scale. This is not a pure ML modeling roleit is about making AI usable, fast, and operationally critical within live healthcare systems. If the model is the brain, this role builds the nervous system.

Key Responsibilities

Design and own low-latency inference APIs (<200ms) for real-time denial prediction and claim scoring.

Build and maintain the claims scrubbing engine that combines payer rules, edits, and AIdriven risk signals.

Integrate AI outputs into Angular-based denial management UI and billing worklists for actionable workflows.

Develop robust API layers connecting ML models with RCM systems (billing, coding, clearinghouses).

Architect high-throughput, fault-tolerant systems for real-time and batch inference.

Implement caching, queuing, and optimization strategies to meet strict latency SLAs.

Own the feedback loop pipeline: capture denial outcomes (835), user corrections, and resubmit signals to improve model accuracy.

Ensure correctness, traceability, and auditability of every prediction and rule applied to a claim.

Monitor system performance (latency, uptime, drift) and proactively resolve bottlenecks.

Collaborate closely with ML engineers, frontend teams, and RCM SMEs to align system behavior with real workflows.

Required Qualifications

510 years of experience in backend or systems engineering, with exposure to ML system deployment.

Strong expertise in building high-performance APIs (Python, Node.js, or Go preferred).

Experience deploying and scaling ML inference systems in production environments.

Deep understanding of system design: latency optimization, caching, concurrency, and distributed systems.

Experience with REST/gRPC APIs, message queues (Kafka, RabbitMQ), and real-time processing.

Familiarity with healthcare data formats (X12 837/835, HL7, FHIR) and RCM workflows.

Experience integrating frontend systems (Angular or similar) with backend APIs.

Strong debugging and performance tuning skills.

Preferred Qualifications

Experience building claims scrubbing engines or rule-based validation systems in RCM.

Exposure to denial prediction or revenue integrity platforms.

Experience with feature stores, model serving frameworks (FastAPI, TensorFlow Serving, TorchServe).

Knowledge of frontend-backend interaction patterns for real-time decision systems.

Experience with cloud infrastructure (AWS/GCP/Azure), containers (Docker, Kubernetes).

Understanding of payer edits, clearinghouse logic, and claim lifecycle timing constraints