Research Vision Expertise

Thinking Machines Lab; San Francisco (CA); On-site; USD 350,000 - 475,000; Full time

Benefits offered by this job

Generous health, dental, and vision benefits

Unlimited PTO

Paid parental leave

Relocation support if needed

Job summary

Thinking Machines Lab in San Francisco is looking for new team members to advance multimodal learning and visual perception science. This role involves developing AI architectures, datasets, and evaluation tools to fuse text and images effectively.

The ideal candidate has a Bachelor's degree in a related field, is proficient in Python, and possesses an ability to analyze experiments with empirical rigor. Generous benefits and a salary range of $350K - $475K are offered.

Qualifications

• Demonstrated research judgment and empirical rigor in machine learning experiments.
• Comfortable with debugging distributed training and writing scalable code.
• Strong theoretical and empirical grounding in relevant disciplines.

Responsibilities

• Own research projects on multimodal AI model training and performance analysis.
• Curate and build datasets to advance vision capabilities.
• Collaborate with data infrastructure engineers and pretraining researchers.
• Publish and present research to share insights with the community.

Skills

Ability to design, run, and analyze experiments thoughtfully

Understanding of machine learning fundamentals

Proficiency in Python

Clarity in communication

Education

Bachelor’s degree in Computer Science, Machine Learning, Physics, Mathematics, or related discipline

Tools

Deep learning frameworks (e.g., PyTorch, TensorFlow, JAX)

Job description

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

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 at scale. We 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.

What You’ll Do

• 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.

Skills and Qualifications

• 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, TensorFlow, 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 vision-language modeling, video understanding, or multimodal AI.
• A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs.
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.

Logistics

• Location: This role is based in San Francisco, California.
• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.

Thinking Machines Lab will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the California Fair Chance Act, the San Francisco Fair Chance Ordinance, and any other applicable state or local fair chance ordinance or law.