Technical Product Manager (Applied Research)

A technical degree in science or engineering (e.g., computer science, computer engineering, software engineering) at Master’s level or above.

3+ years of experience as a product manager working on AI and data products

3+ years of experience working with research teams.

A strong ability to navigate early-stage chaotic environments in a startup, with changing objectives and different levels of complexity

A strong data-driven and problem-solving oriented mindset, and great communicator who thrives working across technical and non-technical teams.

The chance to contribute meaningfully to a globally critical initiative

Comprehensive health benefits (including mental health and wellness management account)

20 days of vacation per year upon start

Employer contribution of 4% to your retirement savings, with no required employee match

Additional compensation totaling 8% of your salary to apply towards additional retirement savings or bonuses (independent of group and individual performance)

A team of passionate world-class experts in their field

Deeply understand the risks posed by frontier models (e.g., LLMs), such as misalignment, alignment faking, sycophancy; as well as how malicious actors may use these systems to do harm to individuals and society at large.

Deeply understand the Scientist AI, how it is different from large language models (LLMs), and how it aims to solve the problems above.

Working together with the research and leadership teams, identify the set of applied use-cases and build an exploratory strategy and roadmap around them.

Engage with external stakeholders (including other research organizations and industry partners) to understand their problems and use-cases for the Scientist AI (what problem they need to solve, how the Scientist AI might do it, what the requirements are, how success is measured, what datasets are available etc.).

Day-to-day carry out project management for the team to effectively iterate and deliver on the applied use-cases (establishing priorities, user stories, specs, deliverables, success metrics etc.).

Where relevant, coach researchers, engineers and the rest of LawZero to ask questions and think about what is important to customers and users, and how they will benefit from the Scientist AI (i.e. “think like a product owner”).