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Hinge Health is seeking a Senior AI Engineer to own the production MLOps and distributed systems that power the company's proactive member experience platform. You will design and operate deployment, serving, release automation, monitoring, rollback, orchestration, and backend integration systems that make AI reliable, observable, scalable, and cost-conscious in production.
In your first three months, you'll build context on Hinge Health's member experience, communication systems, model lifecycle, and data flows. You'll establish baselines for key AI-backed services including service-level objectives, deployment paths, observability, and failure modes. You'll ship targeted improvements to developer experience, testing, release safety, or incident response.
Within six months, you'll own the design and delivery of a production MLOps or distributed-systems initiative from technical plan through rollout and measurement. You'll build or improve safe promotion patterns for model-backed services, including versioning, configuration, feature flags, canaries, rollback, and recovery. You'll partner with ML Scientists and Data Engineering to define durable interfaces, data contracts, feature inputs, freshness expectations, and production-readiness criteria.
Within a year, you'll operate reliable online and batch inference capabilities meeting clear contracts for latency, availability, correctness, resiliency, observability, and cost. You'll create reusable patterns for APIs, services, queues, durable workflows, monitoring, and incident response. You'll lead through influence by shaping technical decisions, mentoring engineers, and connecting system reliability to business outcomes like message relevance and engagement.
You are a senior software engineer with strong backend and distributed-systems experience who enjoys owning systems across their full lifecycle. You're an owner-operator comfortable with on-call, incident response, debugging, and root-cause analysis. You can develop fluency in ML systems to ask strong questions and help model owners bring their work safely into production. You practice effective communication, write clear technical proposals, and lead at all levels by staying hands-on while creating leverage through reusable patterns and mentoring. You care deeply about privacy, security, auditability, and safe handling of sensitive healthcare data.
Hinge Health is an AI-powered digital health platform addressing musculoskeletal (MSK) conditions, available to over 20 million people across 2,800+ employers. The company is headquartered in San Francisco with offices in Montreal and Bangalore.
Hybrid model: 3 days per week in-office, full 8-hour business days. San Francisco office is dog-friendly.
REQUIREMENTS:
- 3+ years of non-internship, full-time professional software engineering experience
- 3+ years designing, building, and operating backend or distributed systems in production, including on-call and incident response participation
- Demonstrated experience deploying and operating ML- or AI-backed services in production
- Strong proficiency in Python and at least one production backend language (TypeScript/JavaScript, Go, Java, or Kotlin)
PREFERRED QUALIFICATIONS:
- Experience building or operating MLOps or ML platform capabilities (inference pipelines, model registries, deployment automation, monitoring, evaluation integration, automated retraining)
- Experience with online inference, batch scoring, recommendation systems, ranking, propensity models, or send-time optimization
- Experience with AWS and production technologies (Kubernetes, Docker, Kafka, PostgreSQL, Airflow, Databricks, MLflow, or equivalents)
- Experience with workflow orchestration and long-running state (Temporal, Step Functions, Cadence, or comparable)
- Experience building observability for ML systems (data quality, feature freshness, model behavior, prediction quality, latency, errors, cost)
- Experience partnering with ML Scientists or Data Scientists to define production interfaces and operate models
- Experience integrating generative AI, LLMs, retrieval, agents, or model evaluation into production products
- Experience in healthcare, digital health, fintech, or regulated/data-sensitive domains; familiarity with PHI, HIPAA, or comparable privacy constraints
