Associate General Counsel, Corporate & Commercial

Thinking Machines Lab•San Francisco, CA

1d•$350,000 - $425,000•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 scientists, engineers, and builders who’ve created some of the most widely used AI products, including ChatGPT and Character.ai, open-weights models like Mistral, as well as popular open source projects like PyTorch, OpenAI Gym, Fairseq, and Segment Anything. About the Role We’re looking for an attorney to lead the corporate function at Thinking Machines Lab. This role will report to the General Counsel. You’ll own two interconnected bodies of work. On the corporate side, you’ll be the company’s primary lawyer for equity and financing transactions, corporate governance, cap table management, board and investor matters, and subsidiary maintenance. On the commercial side, you’ll be the person who turns early-stage deal flow into a scalable contracting function, supporting customer agreements, strategic partnerships, vendor relationships, licensing, data agreements, and research collaborations. This is a hands-on role for someone who likes being close to the business and building the systems that help it move. You’ll work directly with Finance, People, the executive team, and the board on corporate matters, and with product, engineering, and research on commercial ones. The work requires strong judgment across both disciplines, comfort with ambiguity, and the ability to translate complex business needs into practical legal terms.

Requirements

10+ years of legal experience, with significant experience in both corporate transactions (equity financings, governance, M&A) and commercial contracting for technology companies.
Strong background in private company corporate work, including venture and growth equity financings, preferred stock transactions, secondary transactions, corporate governance, and cap table management, alongside experience with technology transactions, SaaS agreements, enterprise contracts, IP licensing, and commercial risk allocation.
Client counseling skills, with the ability to exercise judgment under uncertainty and communicate practical, solution-focused advice.
Strong drafting and negotiation skills, including the ability to explain positions clearly to business teams and resolve issues pragmatically.

Nice To Haves

In-house experience at a technology company shipping products with novel legal risk, ideally on legal issues for AI products, services, or academic research, with knowledge of the machine learning development lifecycle
Corporate law experience at a late-stage private or recently public technology company, including equity financings, board governance, cap table management, and M&A. Familiarity with the full arc of private company corporate work—from Series A mechanics through IPO readiness—is a plus.
Experience with AI-powered products or AI transactions, including compute/infrastructure, licensing, data rights, joint development, research collaborations, or strategic partnerships.
Familiarity with AI-related commercial, IP, data protection, and regulatory issues, with the judgment to spot issues and partner with specialists where needed.

Responsibilities

Lead equity and financing transactions from term sheet through closing, and support M&A and other strategic transactions as they arise.
Own corporate governance, including board matters, cap table, and equity compensation administration, entity maintenance, and the corporate records and policies that keep the company well-governed as it scales.
Structure, draft, and negotiate the commercial agreements that drive Thinking Machines’ business, from partnerships, infrastructure, enterprise to vendors, NDAs, and research collaborations.
Build the legal infrastructure that lets the company move—contracting templates, playbooks, approval workflows, and scalable positions on the issues that come up most, including IP, data use, confidentiality, and risk allocation for AI products.