HR Business Partner

Thinking Machines Lab•San Francisco, CA

21d•$190,000 - $300,000•Onsite

About The Position

Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals. We are a small team of scientists, engineers, and builders who've created some of the most widely used AI products including ChatGPT, Character.ai, and PyTorch. As we scale our team, some of the hardest challenges we face are about empowering and aligning our people and helping our managers make crucial decisions under uncertainty. About the Role The new role of HR Business Partner will combine two tasks: leadership coaching, and people systems design. The two require different skillsets, but a shared vision for managing talent. You will coach managers at Thinking Machines Lab to be more effective leaders. You will provide strategic support on researcher and engineer performance, team dynamics, and personal growth. You will also build the people infrastructure that will scale this support as the company grows: performance and feedback systems, compensation structures, and career frameworks.

Requirements

5+ years experience working as a people leader or HR business partner in a high-growth technical environment.
Fluency with employment law and best practices in North America.
Proven track record of developing HR practices and supporting a diverse set of talent.

Nice To Haves

10+ years experience as a people leader or HR business partner.
Experience in setting up novel systems that scale in a fast-growing company.
Coaching experience that showcases a skill set in providing confidential support, motivating improvement, and conflict resolution.
Track record of supporting a high trust and low ego environment for high caliber talent with strong retention and growth.

Responsibilities

Coach managers by observing how they lead, identifying strengths and blind spots, and working on continuous improvement.
Advise leadership on organizational decisions: team structure, succession planning, and strategic people choices that shape how we work.
Design compensation structures that let us compete for the best machine learning talent in the world while staying aligned on values and principles.
Create career/leveling frameworks that work for a research lab where career advancement often doesn’t mean managing people, contributions such as mentorship and taste are harder to measure, and where senior researchers expect to grow and learn even after a decade in the role.
Build feedback and evaluation processes that help people improve, not just get measured.