AI Engineer

What You Will Do

• Build full-stack AI prototypes that pressure-test venture ideas before founder or entrepreneur-in-residence handoff.
• Design AI systems from composable building blocks and make the tradeoffs visible to product and engineering partners.
• Choose retrieval and context strategies that fit the data and task, from structured queries and hybrid search to reranking, graph traversal, and long-context or human-curated context.
• Build agentic and workflow-based systems with clear control flow, bounded autonomy, useful tool interfaces, state management, recovery paths, and human review where appropriate.
• Make architecture and platform choices that fit the stage of an idea, keeping prototypes cheap to change while leaving a credible path to production if the idea validates.
• Build and integrate APIs, databases, third-party services, internal tools, and cloud infrastructure.
• Define evaluation loops for AI behavior, including task success, retrieval quality, factuality, tool-call correctness, grounding, safety, latency, cost, and user-perceived quality.
• Use error analysis to decide whether to improve prompts, data, retrieval, tools, orchestration, model choice, UX, or product scope.
• Collaborate cross-functionally with product, design, and AI experts to create, test, and iterate on new concepts using direct user feedback.
• Present build results to potential entrepreneurs-in-residence and founders: what worked, what failed, what they need to know to decide next steps.
• Direct frontier coding agents to turn clear product and technical intent into working software, while owning the architecture, review, debugging, and quality bar.
• Identify and troubleshoot issues across the full stack, including frontend, backend, AI orchestration, data pipelines, deployment, and production behavior.
• Contribute to better development processes, reusable engineering practices, and shared technical judgment across the team.

What You Must Bring

• 3+ years of software engineering experience, including end-to-end ownership of at least one production AI application architecture spanning UI, backend, data, models, tools, and evaluation.
• Demonstrated experience building applications that use large language models, multimodal models, or other modern AI capabilities in product workflows.
• Strong technical fluency across frontend, backend, APIs, databases, and cloud deployment, with enough depth to review, debug, and steer implementation.
• Expert ability to work with frontier coding agents, including writing precise specs, decomposing work, inspecting generated code, catching architectural mistakes, and deciding when to intervene directly.
• Ability to justify retrieval choices against corpus structure, freshness, permissions, latency, precision, recall, and cost.
• Experience with SQL and NoSQL data systems, including the ability to model data for application use, retrieval, analytics, and operational reliability.
• Strong communication skills and the ability to work collaboratively across disciplines.
• Habit of reading papers, model cards, technical postmortems, and production writeups, then folding useful lessons into the next build.

Nice To Have

• Experience shipping MVPs, prototypes, or early-stage products under ambiguity.
• Experience as a technical lead, architect, founding engineer, or senior builder on AI-driven products.
• Contributions to open-source AI, developer tools, evals, retrieval, agents, or applied ML infrastructure.
• Interest or experience in product design, product strategy, or company creation.