Thinking Machines Lab is an artificial intelligence research and product company. We're building a future where everyone has access to the knowledge and tools to make AI work for their unique needs and goals.

While AI capabilities have advanced dramatically, key gaps remain. The scientific community's understanding of frontier AI systems lags behind rapidly advancing capabilities. Knowledge of how these systems are trained is concentrated within the top research labs, limiting both the public discourse on AI and people's abilities to use AI effectively. And, despite their potential, these systems remain difficult for people to customize to their specific needs and values. To bridge the gaps, we're building Thinking Machines Lab to make AI systems more widely understood, customizable and generally capable.

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.

Science is better when shared

Scientific progress is a collective effort. We believe that we'll most effectively advance humanity's understanding of AI by collaborating with the wider community of researchers and builders. We plan to frequently publish technical blog posts, papers, and code. We think sharing our work will not only benefit the public, but also improve our own research culture.

AI that works for everyone

Emphasis on human-AI collaboration. Instead of focusing solely on making fully autonomous AI systems, we are excited to build multimodal systems that work with people collaboratively.

More flexible, adaptable, and personalized AI systems. We see enormous potential for AI to help in every field of work. While current systems excel at programming and mathematics, we're building AI that can adapt to the full spectrum of human expertise and enable a broader spectrum of applications.

Solid foundations matter

Model intelligence as the cornerstone. In addition to our emphasis on human-AI collaboration and customization, model intelligence is crucial and we are building models at the frontier of capabilities in domains like science and programming. Ultimately, the most advanced models will unlock the most transformative applications and benefits, such as enabling novel scientific discoveries and engineering breakthroughs.

Infrastructure quality as a top priority. Research productivity is paramount and heavily depends on the reliability, efficiency, and ease of use of infrastructure. We aim to build things correctly for the long haul, to maximize both productivity and security, rather than taking shortcuts.

Advanced multimodal capabilities. We see multimodality as critical to enabling more natural and efficient communication, preserving more information, better capturing intent, and supporting deeper integration into real-world environments.

Learning by doing

Research and product co-design. Products enable iterative learning through deployment, while great products and research strengthen each other. Products keep us grounded in reality and guide us to solve the most impactful problems.

Empirical and iterative approach to AI safety. The most effective safety measures come from a combination of proactive research and careful real-world testing. We plan to contribute to AI safety by (1) maintaining a high safety bar--preventing misuse of our released models while maximizing users' freedom, (2) sharing best practices and recipes for how to build safe AI systems with the industry, and (3) accelerating external research on alignment by sharing code, datasets, and model specs. We believe that methods developed for present day systems, such as effective red-teaming and post-deployment monitoring, provide valuable insights that will extend to future, more capable systems.

Measure what truly matters. We'll focus on understanding how our systems create genuine value in the real world. The most important breakthroughs often come from rethinking our objectives, not just optimizing existing metrics.

Join Us

We're building AI systems that push technical boundaries while delivering real value to as many people as possible. Our team combines rigorous engineering with creative exploration, and we're looking for collaborators to help shape this vision.

You can follow us on X at @thinkymachines, or submit job applications here if you're interested in working with us.

Product

Join us in the exciting early stages of building something transformative. We are looking for people with a strong track record of building successful AI-driven products from the ground up and enthusiasm about wearing multiple hats--building functional product prototypes, crafting smooth UI designs and directing product decisions--to bring cutting-edge AI to the real world.

Core Infrastructure

Help us lay the infrastructure foundation for everything we build, spanning from orchestrating supercomputing clusters to ensuring system robustness under heavy load to support both AI research and product needs. We're looking for engineers with deep experience in building and maintaining fault-tolerant, scalable, and secure infrastructure via load balancing, autoscaling, monitoring, and various elements for service orchestration.

Machine Learning

We're building a small, high-caliber team of machine learning scientists and engineers. The activities will range from building training infrastructure to carrying out exploratory research projects. Whether you hold a PhD or are self-taught, we're interested in candidates who can demonstrate concrete achievements in ML research and engineering through:

  • Research publications in ML
  • Open-source ML implementations
  • Experience building and scaling ML systems