Senior AI Engineer, MLOps, Distributed Systems

Hinge Health

. Build systems that determine what message a member receives, when they receive it, and which channel is most helpful .

Posted 9/30/2026full-timeSan Francisco • California • United StatesSenior💰 $164,800 - $247,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating backend systems, particularly in deploying and managing AI-backed services. Proficient in MLOps practices, with a strong focus on system reliability, observability, and integration of machine learning capabilities into production environments.

Highest-signal resume keywords

Python ProficiencyMLOps ExperienceBackend System DesignAWS TechnologiesIncident Response Participation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonTypeScriptJavaScriptGoJavaKotlinMLOpsMachine Learning DeploymentBackend System DevelopmentObservability

Soft Skills

MentoringTechnical Decision-MakingCollaboration

Tools & Technologies

AWSKubernetesDockerKafkaPostgreSQLAirflowDatabricksMLflow

Industry Keywords

HealthcareDigital HealthFintechPHIHIPAA

Tech Stack

Tools & technologies

AirflowAWSDistributed SystemsDockerJavaJavaScriptKafkaKotlinKubernetesPostgresPythonTypeScriptGo

About the role

Key responsibilities & impact

Build systems that determine what message a member receives, when they receive it, and which channel is most helpful
Own the production path that turns models and AI capabilities into dependable member-facing experiences
Design and operate deployment, serving, release automation, monitoring, rollback, orchestration, and backend integration systems
Build context on the member experience, communication systems, model lifecycle, data flows, and operational requirements
Establish baselines for AI-backed service health, including service-level objectives, deployment paths, observability, failure modes, and operational ownership
Ship improvements to developer experience, testing, release safety, and incident response
Own production MLOps or distributed-systems initiatives from technical planning through rollout, measurement, and iteration
Build or improve safe promotion patterns including versioning, configuration, feature flags, canaries, rollback, and recovery
Define durable interfaces, data contracts, feature inputs, freshness expectations, evaluation hooks, and production-readiness criteria
Operate online and batch inference capabilities meeting contracts for latency, availability, correctness, resiliency, observability, and cost
Create reusable patterns for APIs, services, queues, durable workflows, monitoring, and incident response
Integrate AI into notifications, personalization, send-time optimization, and related experiences
Shape technical decisions, mentor engineers, conduct design and code reviews, and improve engineering workflows
Connect system reliability to message relevance, engagement, reduced communication fatigue, and sustained participation in care

Requirements

What you’ll need

3+ years of non-internship, full-time professional software engineering experience
3+ years designing, building, and operating backend or distributed systems in production
Experience participating in on-call and incident response
Demonstrated experience deploying and operating ML- or AI-backed services in production
Strong proficiency in Python
Proficiency in at least one production backend language such as TypeScript/JavaScript, Go, Java, or Kotlin
Experience with AWS and production technologies such as Kubernetes, Docker, Kafka, PostgreSQL, Airflow, Databricks, MLflow, or equivalent systems
Preferred: Experience building or operating MLOps or ML platform capabilities
Preferred: Experience with online inference, batch scoring, recommendation systems, ranking, propensity models, or send-time optimization
Preferred: Experience with workflow orchestration and long-running state
Preferred: Experience building observability for ML systems
Preferred: Experience partnering with ML Scientists or Data Scientists to define production interfaces
Preferred: Experience integrating generative AI, LLMs, retrieval, agents, or model evaluation into production products
Preferred: Experience in healthcare, digital health, fintech, or another regulated or data-sensitive domain
Preferred: Familiarity with PHI, HIPAA, or comparable privacy constraints
Willingness to work from the San Francisco office 3 days a week for the full 8 hours of a typical business day
Located in or willing to relocate to the SF Bay Area
Legally authorized to work in the United States

Benefits

Comp & perks

Comprehensive medical, dental, and vision coverage
Gender-affirming care support
Family and fertility planning tools
Travel reimbursements if healthcare isn’t available where you live
Traditional or Roth 401k retirement plan options
2% company 401k match
Learning and development stipend
Discounted company stock through ESPP
Dog-friendly workplace program at the San Francisco office
Reasonable accommodations for candidates with disabilities