Senior AI Engineer

Re:Shark builds AI agents that execute revenue work for B2B companies. We have a live product, paying customers, our own data infrastructure, and our own outbound stack. We're now hiring our first dedicated AI engineer to work directly with the CTO on the next generation of the system. What you'll actually work onThe problems are concrete: Entity resolution & context gathering: We maintain a massive graph of companies and people. You'll build the crawlers, scrapers, and extraction pipelines to turn messy, conflicting real-world data into structured, trustworthy context.Timing intelligence: Companies change constantly. You'll build fast, measurable systems that detect market shifts (like new executives or products) and turn that noise into useful commercial decisions.Production AI agents: Our agents don't operate in a demo environment; their decisions lead to real outcomes like replies, meetings, bounces, and revenue. You'll build the evaluation, tooling, and feedback loops to understand where they succeed and where they fail.AI under real constraints: The best model isn't automatically the right model. You will evaluate models, design experiments, and balance quality, latency, reliability, and cost, while knowing when an AI model isn't actually the right tool for the job. How we workProblems, not tickets: You take rough ideas to production and own what happens next. The problem doesn't respect layer boundaries, and neither do we.The stack: PHP, TypeScript, Rust, Python, and PostgreSQL. You don't need to arrive as an expert in all of them, but you must be able to jump into unfamiliar codebases and solve the problem.Rapid iteration: We prototype aggressively, test against real data, ship, measure, and iterate. Your work encounters reality quickly.High autonomy: You'll work directly alongside the CTO, who is deeply hands-on in the codebase. Expect direct communication, fast decisions, and underspecified problems where figuring out the actual issue is part of the job. You might be a great fit ifYou're an excellent software engineer first, with strong experience building and maintaining LLM-based systems in production.You know how to evaluate AI systems using real data, benchmarks, and experiments.You understand enough machine learning and statistics to reason about model quality, accuracy, latency, reliability, and cost together.You're comfortable spanning backend systems, data infrastructure, and AI.You move quickly, take ownership, and prefer shipping and measuring over debating the perfect architecture for three weeks. This probably isn't for you ifYou only want to work on model research, write Python, or build AI demos.You consider backend, data, or infrastructure to be somebody else's problem.You need detailed tickets before you can start, or you prefer to stay inside one narrow technical specialty.You aren't comfortable taking responsibility for a system after it ships.