Jobs / United States / Hinge Health INC
Senior AI Engineer, MLOps & Distributed Systems
Hinge Health INC · 🇺🇸 San Francisco-HQ
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 13 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 6 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Hinge Health INC paid sponsored hires in similar roles5 certified filings for “Senior Software Engineer - React Native” (Software Developers) in CA: $188k–$265k, median $214k. Most were filed at wage level IV (60%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Hinge Health INC
The US Department of Labor certified 13 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 6 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Hinge Health INC →
About the role
SENIOR AI ENGINEER, MLOPS & DISTRIBUTED SYSTEMS THE OPPORTUNITY Join 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 ACCOMPLISH IN 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