Senior AI Engineer
$136,800–$136,800 year
HybridSan Francisco, California, United States
Job Summary
Design, build, and operate cloud infrastructure and deployment systems for multi-modal models and reasoning agents in production. Manage model serving architectures, agent orchestration services, and evaluation pipelines to validate quality, monitor hallucination risks, and ensure compliance. Establish telemetry and observability using modern platforms while optimizing throughput, capacity, and cost efficiency for millions of active users. Collaborate with ML scientists, platform engineers, and SRE partners to translate research systems into secure, scalable product capabilities. Work in a hybrid model requiring three days in the office, supporting Hinge Health's mission to automate healthcare delivery for musculoskeletal conditions.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related field
- 3+ years of experience developing and operating cloud-based services, infrastructure, and APIs in production, ideally on AWS
- Experience deploying and operating production ML systems, especially LLMs, VLMs, or other large-scale model systems, including release management, evaluation, and monitoring
- Experience with observability and operational tooling such as monitoring, logging, tracing, and alerting platforms
- Demonstrated experience with CI/CD and production deployment workflows across development, staging, and production environments
- Must be able to work in the office 3 days/week
Desired Qualifications
- Experience using agentic development workflows, including AI-assisted coding and review (e.g. via Claude), plus reusable skills or agents to improve velocity and quality
- Experience with LLM and agent development tooling such as LangSmith, LangChain, LangGraph, or MLflow
- Experience with GPU-backed inference systems, model serving optimization, and scaling for latency-sensitive applications
- Experience deploying or integrating hosted model APIs such as Anthropic, Gemini, or Bedrock
- Experience building validation and telemetry systems for generative AI, including regression testing, quality scoring, and production monitoring
- Experience with containerized services and orchestration technologies such as Docker, Kubernetes, ECS, or EKS
- Experience with workflow orchestration tools such as Temporal or Step Functions
- Experience with Databricks or similar platforms for data, experimentation, evaluation, or ML platform operations
- Experience with IAM, secrets management, encryption, and compliance-minded cloud controls
- Experience with infrastructure as code, especially Terraform, and agent infrastructure such as orchestration, tool use, execution control, memory/state handling, and guardrails
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