Senior AI Engineer — Hybrid MLOps & Distributed Systems

Hinge Health; San Francisco (CA); Hybrid; USD 170,000 - 250,000; Full time

Benefits offered by this job

Healthcare benefits

401k with match

Learning stipend

Employee stock purchase plan

Job summary

Hinge Health is seeking a Senior AI Engineer focused on MLOps & distributed systems to own the production path turning AI capabilities into reliable member experiences. You will design deployment, serving, monitoring, and rollback systems to keep AI features safe, observable, and cost-conscious in production.

You’ll collaborate with ML Scientists, Data Engineers, and Software Engineers to ship durable interfaces, feature inputs, and production-ready criteria for model-backed services.

Qualifications

• 3+ years of non-internship, full-time software engineering experience.
• 3+ years designing, building, and operating backend or distributed systems in production, including on-call experience.
• Proven experience deploying and operating ML- or AI-backed services in production, with strong Python and at least one production language (TypeScript/JavaScript, Go, Java, or Kotlin).

Responsibilities

• Own the production path for models and AI capabilities, ensuring reliable, observable systems.
• Design and operate deployment, serving, release automation, monitoring, rollback, and orchestration.
• Collaborate with ML Scientists, Data Scientists, Data Engineering, Product, and Software Engineering to ship safe, scalable AI features.
• Lead production MLOps or distributed-systems initiatives from plan to rollout, measurement, and iteration.
• Define durable interfaces, data contracts, and production readiness criteria with stakeholders.

Skills

Python

Backend development

Distributed systems

On-call / incident response

ML/AI production

Frontend/backends integration

Communication

Tools

AWS

Kubernetes

Docker

Kafka

PostgreSQL

Airflow

Databricks

MLflow

Job description

Hinge Health is seeking a Senior AI Engineer focused on MLOps & distributed systems to own the production path turning AI capabilities into reliable member experiences. You will design deployment, serving, monitoring, and rollback systems to keep AI features safe, observable, and cost-conscious in production.

You’ll collaborate with ML Scientists, Data Engineers, and Software Engineers to ship durable interfaces, feature inputs, and production-ready criteria for model-backed services.