Research, Vision Expertise San Francisco

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.

Thinking Machines builds multimodal‑first. We’re looking for new team members to advance the science of visual perception and multimodal learning. We think about how vision and language interact  design architectures that fuse pixels and text, build datasets and evaluation methods that test real‑world comprehension, and develop representations that let models ground abstract concepts in the physical world. Our goal is to create multimodal systems that support seamless integration into real‑world environments.

You’ll work at the intersection of visual understanding, multimodal reasoning, and large‑scale model training. You’ll help develop the architectures, data, and evaluation tools that teach AI to see, understand, and collaborate. The best candidate is curious about multimodal interfaces, has experience running large‑scale experiments and is comfortable contributing to complex engineering systems. While we are looking for a person with expertise in multimodality, Thinking Machines Lab operates in a unified fashion and expects new hires to work across modalities as one team.

This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high‑performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands‑on experimentation, and who wants to shape the foundations of how AI learns.

Note:

This is an "evergreen role" that we keep open on an on‑going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months.

You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you’re welcome to apply directly in addition to an evergreen role.

• Own research projects on training and performance analysis of multimodal AI models.
• Curate and build large‑scale datasets and evaluation benchmarks to advance vision capabilities.
• Work with our data infrastructure engineers, pretraining researchers and engineers, and product team to create frontier multimodal models and the products that leverage them.
• Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia.

• Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor.
• Understanding of machine learning fundamentals, large‑scale training, and distributed compute environments.
• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, Tensor Flow, or JAX). Comfortable with debugging distributed training and writing code that scales.
• Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
• Clarity in communication, an ability to explain complex technical concepts in writing.

Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some:

• Research or engineering contributions in visual reasoning, spatial understanding, or multimodal architecture design.
• Publications or open‑source contributions in…