bjakcareer - Technical Product Manager, AI Systems
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Requirements
• Hands-on exposure to AI-powered products, including LLM-based systems. • Experience working with model evaluation, prompt or pipeline iteration, and feedback loops. • Strong intuition for model limitations, hallucinations, bias, and drift. • PRODUCT LEADERSHIP • Significant experience owning complex, technical products end-to-end. • Proven ability to work closely with senior engineers and ML teams. • Strong judgment and decision-making ability in ambiguous, fast-moving environments. • Ability to balance ambition with technical and operational reality. • Experience shipping AI-heavy consumer products. • Background as an engineer or highly technical product manager. • Experience defining evaluation metrics for ML systems. • Strong intuition for AI UX patterns and failure handling. • Prior experience in zero-to-one product environments. • The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product
Responsibilities
• Own the end-to-end AI product strategy, grounded in technical feasibility and real-world constraints. • Translate model capabilities, data limitations, and evaluation results into clear product decisions. • Make hard trade-offs across quality, latency, cost, reliability, and user experience. • Work daily with ML, backend, and mobile engineers on design, evaluation, and iteration. • Define success metrics and feedback loops across offline evaluation, online experiments, and human feedback. • Drive execution with clear specifications, risk awareness, and disciplined prioritization. • Ensure AI features ship quickly, safely, and reliably into production. • Own AI product quality across UX, correctness, and outcomes. • TECHNICAL FOUNDATION • Strong grounding in computer science fundamentals, including algorithms, data structures, and system design. • Solid understanding of ML fundamentals and how modern AI systems behave in production. • Comfort reading, reviewing, and discussing technical design documents.
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