Head Of Product (AI)

TL;DR

Head Of Product (AI): Defining and driving the end-to-end strategy for a proactive AI chat application with an accent on technical feasibility, model constraints, and real-world task reliability. Focus on making complex trade-offs between system performance and user experience while collaborating directly with engineering teams to ship high-quality AI features.

Location: Must be based in or able to commute to Palo Alto, California

Company

A technology company building proactive AI tools for everyday task automation and persistent context management.

What you will do

• Own the end-to-end AI product strategy, balancing technical feasibility with real-world user needs.
• Translate model capabilities and evaluation data into concrete product decisions.
• Work daily with ML, backend, and mobile engineers on design, evaluation, and iteration.
• Define and manage success metrics across offline evaluation and online experiments.
• Execute product roadmaps with disciplined prioritization and risk management.
• Maintain high product quality standards across UX, model correctness, and task outcomes.

Requirements

• Significant experience owning complex, technical products end-to-end.
• Solid understanding of ML fundamentals and how modern AI systems behave in production.
• Strong computer science foundation, including algorithms, data structures, and system design.
• Proven ability to collaborate closely with senior engineering and ML teams.
• Strong judgment and decision-making ability in ambiguous, fast-moving environments.
• Must be able to work in a hybrid environment in Palo Alto.

Nice to have

• 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.

Culture & Benefits

• Work within a high-talent-density team focused on shipping world-class products.
• Collaborative decision-making culture with rapid execution speed.
• Focus on balance between high-quality output and iterative learning.

Hiring process

• Evaluation of application by technical team members.
• 3 to 4 interviews conducted via virtual meetings and/or onsite.
• Prompt decision-making process.