Role overview
Work model: Remote, with no travel required
Pay: $215,000 - $250,000/year USD base salary
What this role involves
Build and deliver AI-powered features for a personalized health and fitness platform. This hands-on engineering role takes capabilities from initial concept through experimentation, evaluation, deployment, monitoring, and ongoing improvement.
You will partner with product, mobile, and backend teams to identify useful applications of AI, validate prototypes with real users, and develop reliable production services. The work balances quality, safety, personalization, latency, and operating costs.
Engineering responsibilities
- Build production AI agents using tool-calling LLMs, structured outputs, parallel API orchestration, and streaming responses.
- Develop evaluation harnesses and quality scoring with Langfuse and rubrics that measure safety, effectiveness, and personalization.
- Prototype agent capabilities, validate them through evaluations, deploy to staging and production, monitor traces, and iterate.
- Improve reliability, latency, and cost through prompt caching, token budgets, retry logic, and observability.
- Develop agent tools, including API integrations with Pydantic validation, exercise search over local databases, and structured workout submission.
Technical qualifications
- Strong Python skills and experience building and deploying services within large production systems.
- Experience with LangChain, LangGraph, or similar agent frameworks.
- Hands-on production LLM experience covering prompt engineering, tool or function calling, structured outputs, and evaluation.
- Comfort with asynchronous Python, HTTP APIs, server-sent events, and webhooks.
- Experience with data validation and schema design using tools such as Pydantic and JSON Schema.
- Ability to troubleshoot across application and infrastructure layers, from failed LLM tool calls to Terraform configuration issues.
- Clear communication and effective collaboration with product, mobile, and backend engineers.
Helpful additional experience
- AWS services such as Bedrock, ECR, CloudFront, S3, and Cognito, or other cloud platforms for hosting agents.
- Observability and tracing with Langfuse, OpenTelemetry, or Datadog.
- Evaluation frameworks, including LLM-as-a-judge, automated scoring, and dataset management.
- Infrastructure as code with Terraform or CDK.
Benefits and development
- Equity participation.
- Medical, dental, vision, and disability insurance, plus tax-advantaged savings accounts for eligible employees.
- 401(k) retirement plan.
- Monthly health and fitness stipend.
- Access to mental health resources and therapy support.
- Reimbursement for medical travel.
- Annual learning and development budget for courses, conferences, coaching, and tools.
- Flexible paid time off.
- Complimentary membership to the company's health and fitness platform.