Product Engineer

• Ownership · The hire will own a whole product area, system or major decisions end to end. “We're looking for someone who solves problems across the board: building and improving our applications, shaping products, internal tools, fast prototypes, and …”
• Stated working conditions · Several working conditions are stated. “We build software used across college athletics, professional sports, higher education, and other organizations, from reporting and compliance to live event ope…”
• Open to adjacent experience · Some requirements are marked as optional or nice to have. “Keep AI features fast, reliable, and cost-effective under real production load”

Based only on the posting text; anything it does not mention is left unrated.

Original text

We build software used across college athletics, professional sports, higher education, and other organizations, from reporting and compliance to live event operations. We're a team of < 15 people, with no venture capital, and profitable. This means we get to make decisions for our customers and our team, not for investors. Rails is at the core of everything we build, and we keep reasonable hours and a codebase we're proud of.We're looking for someone who solves problems across the board: building and improving our applications, shaping products, internal tools, fast prototypes, and the systems that keep quality and security high. You'd own problems end to end, from understanding what the customer actually needs to shipping the fix.On the AI side, we want someone who knows how to:- Evaluate new models as they ship, and pick the right one for the job (fast and cheap vs. most capable)- Design agents and multi-agent workflows with tool use, and know when a simpler approach is better- Build retrieval that stays accurate as the underlying content changes (RAG, embeddings, search)- Measure quality with evals and observability, so changes are proven rather than guessed- Keep AI features fast, reliable, and cost-effective under real production load- Handle AI security properly: prompt injection, data isolation between customers, and sensitive data problem.

• Evaluate new models as they ship, and pick the right one for the job (fast and cheap vs. most capable)
• Design agents and multi-agent workflows with tool use, and know when a simpler approach is better
• Build retrieval that stays accurate as the underlying content changes (RAG, embeddings, search)
• Measure quality with evals and observability, so changes are proven rather than guessed
• Keep AI features fast, reliable, and cost-effective under real production load
• Handle AI security properly: prompt injection, data isolation between customers, and sensitive data

Stack today: Rails 8, Hotwire, Hotwire Native, Postgres, AWS. Rails experience helps but isn't required. What matters more is a strong grasp of solid engineering practices and principles, and the judgment to use the right tools to solve the problem.