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. • Product strategy clearly aligns AI capabilities with user needs and company priorities. • AI features deliver real value, are understandable, predictable, and trusted by users. • Decisions balance quality, speed, cost, and reliability effectively under uncertainty. • Roadmaps and priorities are clear, with fast iteration based on real user feedback. • Teams are aligned, focused, and able to execute on AI product goals with minimal friction. • 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.
Responsibilities
• Research and define end-to-end AI system requirements from capability to behavior to user impact • Translate model capabilities, data constraints, and evaluation results into clear product and system decisions • Make hard trade-offs across quality, latency, cost, reliability, and UX • Work closely with ML, backend, and mobile engineers on system design, evaluation, and iteration • Define and evolve evaluation frameworks across offline metrics, online experiments, and human feedback • Drive execution with clear specs, strong judgment, and disciplined prioritization • Ensure systems ship quickly, safely, and reliably, with strong feedback loops • Own product quality end-to-end - correctness, predictability, and user trust • 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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