Applied AI Engineer

We're looking for an Applied AI Engineer to help us build and ship AI-powered features that directly improve our product experience and business outcomes. This is a hands-on, product-focused role where you'll take ideas from concept to production — designing intelligent systems, validating them with real users, and turning them into reliable, scalable services.

You'll work at the intersection of AI, product, and engineering — partnering closely with cross-functional teams to identify high-impact opportunities, prototype quickly, and iterate based on data. This isn't a research-only role. You'll own the full lifecycle: experimentation, evaluation, deployment, monitoring, and continuous improvement.

The ideal candidate is excited about applying LLMs and modern ML tooling to real-world problems. You think in terms of systems, tradeoffs, and outcomes — not just models. You care about performance, quality, latency, and cost in production. Most importantly, you're motivated by shipping impactful AI experiences that customers actually use.

• →Build and ship AI agents that serve real users: tool-calling LLM systems with structured output, parallel API orchestration, and streaming responses.
• →Design evaluation harnesses and quality scoring — we use Langfuse, rubrics to measure safety, effectiveness, and personalization.
• →Own the full loop: prototype a new agent capability, validate it with evals, deploy it to staging and production, monitor traces, and iterate.
• →Improve reliability, latency, and cost through prompt caching strategies, token budgets, retry logic, and observability.
• →Write the tools agents use: API integrations with Pydantic validation, exercise search over local databases, structured workout submission.

• Strong Python skills: you've built and deployed services on large production systems.
• Experience with LangChain/LangGraph or similar agent frameworks.
• Hands-on experience with LLMs in production: prompt engineering, tool/function calling, structured output, evaluation.
• Comfort with async Python, HTTP APIs, and streaming protocols (SSE, webhooks).
• Experience with data validation and schema design (Pydantic, JSON Schema).
• Ability to debug across layers: from a broken LLM tool call to a misconfigured Terraform resource.
• Clear communication: you'll work directly with product, mobile, and backend engineers.

• Familiarity with AWS (Bedrock, ECR, CloudFront, S3, Cognito) or other cloud agent hosting.
• Observability and tracing tools (Langfuse, OpenTelemetry, Datadog).
• Exposure to evaluation frameworks: LLM-as-a-judge, automated scoring, dataset management.
• Infrastructure-as-code (Terraform, CDK).

Equity: Meaningful equity package.
Health Coverage: All employees who meet eligibility requirements receive comprehensive medical, dental, vision, and disability insurance, plus tax-advantaged savings accounts.
Retirement: 401(k) plan.
Wellness: Monthly wellness stipend for fitness, recovery, and overall wellbeing, plus access to mental health resources and therapy support.
Flexible Time Off: Flexible PTO so you can rest, recharge, and take care of life outside of work.
Learning & Development: Annual budget for courses, conferences, coaching, and tools that help you grow in your craft.
Future Membership: Enjoy our platform for free.