<div class="content-intro"><p><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;">At PrizePicks, we are the fastest-growing sports company in North America, as recognized by Inc. 5000. As the leading platform for Daily Fantasy Sports, we cover a diverse range of sports leagues, including the NFL, NBA, and Esports titles like League of Legends and Counter-Strike. Our team of over 550 employees thrives in an inclusive culture that values individuals from diverse backgrounds, regardless of their level of sports fandom. Ready to reimagine the DFS industry together? </span></p></div><h2><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 18pt;">Overview</span></h2> <p> </p> <p><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;">We're building an Applied AI engineering team focused on turning modern ML/LLMs into <strong>reliable, scalable product capabilities</strong>. This is a senior <strong>individual contributor</strong> role for someone who has operated on an Applied AI/Product AI team before — someone who can build, ship, and iterate quickly, while also raising the bar for engineering rigor and judgment in an agent-driven SDLC.</span></p> <p><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;">In the near term, you'll help us build our <strong>agent platform and internal tools</strong>, and partner with application engineering teams to operationalize AI across the <strong>software development lifecycle</strong>. Over time, the Applied AI team will increasingly shape <strong>product roadmap and strategy</strong> for AI-powered experiences — and increasingly, how the rest of engineering works day to day.</span></p> <h3><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 18pt;">What you'll do</span></h3> <ul> <li style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><strong>Build our agent platform and tooling</strong></span> <ul> <li style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;">Design and implement primitives like orchestration, tool/function calling, evaluation harnesses, prompt/version management, tracing/observability, and safety/guardrails.</span></li> <li style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;">Support patterns like retrieval-augmented generation (RAG), structured extraction, and production inference workflows.</span></li> </ul> </li> <li style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><strong>Operationalize AI in the SDLC</strong></span> <ul> <li style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;">Work with application engineering teams to embed AI into day-to-day engineering workflows (code review assist, test generation, incident support, developer copilots), with clear quality gates and measurable impact.</span></li> <li style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;">Own the judgment call of <strong>what to delegate to agents vs. keep as human work</strong> — scope tasks so agents are set up to succeed, and adapt that boundary as team skill levels and tooling maturity vary.</span></li> </ul> </li> <li style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><strong>Reason from the trace, not just the output</strong></span> <ul> <li style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;">Given an agent run, diagnose whether it did its job well from the trace itself — not just the final diff. Debug agent behavior the way you'd debug a distributed system: inputs, intermediate steps, failure points.</span></li> <li style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;">Treat AI output as a claim to verify, not a result to trust — including tests the agent writes for its own code.</span></li> </ul> </li> <li style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><strong>Ship applied AI features end-to-end</strong></span> <ul> <li style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;">Own projects from prototype → production: data needs, system design, model/vendor selection, rollout plans, monitoring, and iteration.</span></li> <li style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;"><span style="font-family: tahoma, arial, helvetica, sans-serif; font-size: 12pt;">Partner closely with product, desig