Senior AI Engineer, MLOps & Distributed SystemsThe OpportunityJoin the team building the intelligence behind Hinge Health's proactive member experience. The Proactive Communications & Notifications pod is creating the systems that determine what message a member receives, when they receive it, and which channel is most helpful. When these decisions are timely, relevant, and respectful, they can help members stay engaged with care and make progress toward better health outcomes.
As a Senior AI Engineer focused on MLOps and distributed systems, you will own the production path that turns models and AI capabilities into dependable member-facing experiences. You'll design and operate the deployment, serving, release automation, monitoring, rollback, orchestration, and backend integration systems that make AI reliable, observable, scalable, and cost-conscious in production.
You'll work closely with ML Scientists, Data Scientists, Data Engineering, Product, and Software Engineering. This role is a strong fit for an MLOps-oriented software engineer who brings deep backend and distributed-systems judgment. You do not need to be a research scientist or own model training strategy; your impact will come from making AI capabilities safe to ship, easy to operate, and straightforward for the team to improve.
What You'll AccomplishIn your first 3 months- Build context on Hinge Health's member experience, communication systems, model lifecycle, data flows, and operational requirements.
- Establish a clear baseline for the health of key AI-backed services, including service-level objectives, deployment paths, observability, failure modes, and operational ownership.
- Ship targeted improvements to developer experience, testing, release safety, or incident response that make the team faster and more predictable.
In your first 6 months- Own the design and delivery of a production MLOps or distributed-systems initiative from technical plan through rollout, measurement, and iteration.
- Build or improve safe promotion patterns for model-backed services, including versioning, configuration, feature flags, canaries, rollback, and recovery.
- Partner with ML Scientists and Data Engineering to define durable interfaces, data contracts, feature inputs, freshness expectations, evaluation hooks, and production-readiness criteria.
In your first year- Operate reliable online and batch inference capabilities that meet clear contracts for latency, availability, correctness, resiliency, observability, and cost.
- Create reusable patterns for APIs, services, queues, durable workflows, monitoring, and incident response that help the pod integrate AI into notifications, personalization, send-time optimization, and related experiences.
- Lead through influence by shaping technical decisions, mentoring engineers, raising the quality bar through design and code review, and connecting system reliability to outcomes such as message relevance, engagement, reduced communication fatigue, and sustained participation in care.
Who You Are- You are a senior software engineer with strong backend and distributed-systems experience, and you enjoy owning systems across their full lifecycle-from architecture and implementation through launch and operations.
- You are an owner-operator who is comfortable with on-call, incident response, debugging, root-cause analysis, and the follow-through required to prevent recurring failures.
- You are a learn-it-all who can develop enough fluency in ML systems to ask strong questions, clarify interfaces, evaluate trade-offs, and help model owners bring their work safely into production.
- You practice effective communication: you write clear technical proposals, make risks and decisions visible, and explain complex system behavior to technical and non-technical partners.
- You lead at all levels by staying hands-on while creating leverage through reusable patterns, thoughtful code reviews, pairing, mentoring, and practical improvements to the team's engineering workflow.
- You care deeply about privacy, security, auditability, and the safe handling of sensitive healthcare data.
Basic Qualifications- 3+ years of non-internship, full-time professional software engineering experience.
- 3+ years designing, building, and operating backend or distributed systems in production, including experience participating in on-call and incident response.
- Demonstrated experience deploying and operating ML- or AI-backed services in production, with strong proficiency in Python and at least one production backend language such as TypeScript/JavaScript, Go, Java, or Kotlin.
Preferred Qualifications- Experience building or operating MLOps or ML platform capabilities, such as inference pipelines, model registries, deployment automation, monitoring, evaluation integration, or automated retraining.
- Experience with online inference, batch scoring, recommendation systems, ranking, propensity models, or send-time optimization.
- Experience with AWS and production technologies such as Kubernetes, Docker, Kafka, PostgreSQL, Airflow, Databricks, MLflow, or equivalent systems.
- Experience with workflow orchestration and long-running state, including Temporal, Step Functions, Cadence, or a comparable system.
- Experience building observability for ML systems, including data quality, feature freshness, model behavior, prediction quality, latency, errors, and cost.
- Experience partnering with ML Scientists or Data Scientists to define production interfaces and operate models without owning model research or training.
- Experience integrating generative AI, LLMs, retrieval, agents, or model evaluation into production products.
- Experience in healthcare, digital health, fintech, or another regulated or data-sensitive domain; familiarity with PHI, HIPAA, or comparable privacy constraints.
Hinge Health Hybrid ModelWe believe that remote work and in-person work have their own advantages and disadvantages, and we want to be able to leverage the best of both worlds.
Employees in hybrid roles are required to be in the office 3 days per week, for the full 8 hours of a typical business day.
The San Francisco office has a dog-friendly workplace program.
