Applied AI Engineer

About Future At Future, we believe that personal connection is the key to progress. Our mission is to make world-class coaching accessible to everyone, helping people unlock their full potential through expert guidance, motivation, and accountability. As a digital personal training platform, we deliver highly personalized coaching, tailoring workout plans and support to each individual’s goals - all through a seamless app experience. Since launching in 2017, Future has grown from a brainstorm in a San Francisco cafe into the nation’s largest provider of personal training sessions. In January 2025, Future announced its merger with Autograph, the company founded by 7x World Champion and entrepreneur Tom Brady. We’re poised for massive growth as we expand our brand, forge new partnerships with some of the world’s most iconic athletes, and harness AI to enhance and scale our coaching experience. As we continue to grow, we’re investing in cutting-edge technology, deepening our roster of elite coaches, and building new partnerships. If you're passionate about shaping the future of fitness, come join us - we’re just getting started. About the Role 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. What You'll Do 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. What You Bring 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. Nice to Have 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). Compensation & Benefits We believe great people should be well paid and meaningfully invested in what they're building. Base Salary $ 215,000– $ 250,000/ year + equi