Jobs at Thinking Machines Lab

The Associate General Counsel will develop privacy strategies, advise on legal issues in AI development, and create legal frameworks for operational compliance in AI systems.

The role involves building tools for AI-assisted software development, optimizing workflow, and maintaining secure development environments.

Lead a team of engineers to design and scale reliable ML systems, ensure high execution standards, and collaborate with tech leads and product teams.

The HR Business Partner will coach managers, advise on organizational decisions, design compensation structures, and build evaluation processes to support employee growth and performance.

The designer will create end-to-end product experiences, collaborate on AI model training, and contribute to design systems and branding.

The Compensation Partner will design and implement compensation frameworks, advise on pay decisions, and develop equity compensation strategies to attract and retain talent.

The Executive Business Partner will manage calendars, coordinate travel, support recruiting, and track projects for technical leaders in a fast-paced startup environment.

The Recruiting Coordinator will manage scheduling, maintain data integrity in recruiting tools, and support various recruiting operations while ensuring a positive candidate experience.

The Technical Sourcer will identify and engage exceptional technical candidates, develop sourcing strategies, and build community engagement for recruitment efforts.

As a Research Product Manager, drive complex research products, translate technical ideas into plans, collaborate across disciplines, and maintain progress communication.

The role focuses on designing and implementing data curation techniques for AI pre-training datasets, ensuring quality, collaboration, and continuous improvement of data processing systems.

The Research Engineer will design, optimize, and scale systems for large AI models, enhancing performance, reliability, and efficiency in model inference and deployment.

Design and optimize large-scale distributed training infrastructure, focusing on numerical methods to enhance efficiency and stability in AI model training.

The role involves designing and optimizing distributed training systems for large models to enhance research efficiency and productivity in AI.

Develop and maintain full stack applications focusing on APIs, UX, system reliability, security, and collaboration across teams.

Design, build, and operate GPU supercomputing environments, automate large GPU clusters, and collaborate with researchers to optimize performance and resources.

The Software Engineer, Data Infrastructure will design scalable systems for data processing and orchestration, collaborating with teams to enhance data quality and accelerate research.

Design and build infrastructure for scalable reinforcement learning, optimize RL training pipelines, and collaborate with researchers on production-grade solutions.

The role involves designing and optimizing ML kernels for large-scale language model training, collaborating on algorithmic design, and enhancing compute efficiency.

As a Software Engineer, you'll develop and maintain systems for AI models, enhance infrastructure, and collaborate across teams on product development.