Research, General Agents

Bison; San Francisco (CA); Presencial; USD 400.000 - 500.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health benefits

Dental benefits

Vision benefits

Unlimited PTO

Paid parental leave

Relocation support

Descripción de la vacante

Thinking Machines in San Francisco is seeking a senior researcher to advance the agentic capabilities of our frontier models, owning the full development cycle from data pipelines to model evaluation. You will build synthetic data frameworks, validate changes through ablations, and improve end-to-end usability for agentic capabilities, collaborating with a small research team.

Eligible candidates have strong engineering skills, Python proficiency, experience with DL frameworks, and a PhD or

Formación

• Strong engineering skills, ability to contribute code and debug in complex codebases.
• Ability to design, run, and interpret experiments with scientific rigor and clarity.
• 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.
• Experience building synthetic data pipelines and systems that were adopted by others on your team and remain in use today.
• Experience owning the end-to-end cycle of identifying gaps in model usability and closing them through custom evaluations and training data.
• Experience making large-scale agentic RL infrastructure reliable given the long tail of failures that surface at scale.
• Experience improving the agentic capabilities of a frontier model.
• PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.

Responsabilidades

• Build a synthetic data framework used across the team, and create new task environments to scale training across agentic capabilities such as tool use, long-horizon tasks, and complex workflows.
• Address identified gaps through data and recipe work, validating proposed changes through controlled ablations.
• Enhance usability for agent capabilities end to end, translating internal and external feedback into model improvements.

Conocimientos

Strong engineering skills

Experiment design & interpretation

Python proficiency

Deep learning framework familiarity

Clear communication

Synthetic data pipelines

End-to-end cycle ownership

RL infrastructure reliability

Agentic capabilities improvements

PhD or equivalent research experience

Educación

Bachelor's degree in CS / ML / Physics / Mathematics

PhD in CS / ML / Physics / Mathematics or equivalent experience

Descripción del empleo

About Thinking Machines

The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.

About the Role

This role is responsible for advancing the agentic capabilities of our models, with ownership spanning the full development cycle. Our research team is small, and the role carries a corresponding degree of autonomy and responsibility.

What You'll Do

• Build a synthetic data framework used across the team, and create new task environments to scale training across agentic capabilities such as tool use, long-horizon tasks, and complex workflows.

• Address identified gaps through data and recipe work, validating proposed changes through controlled ablations.

• Enhance usability for agent capabilities end to end, translating internal and external feedback into model improvements.

Skills and Qualifications

Minimum qualifications:

• Strong engineering skills, ability to contribute code and debug in complex codebases.

• Ability to design, run, and interpret experiments with scientific rigor and clarity.

• 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.

• Experience building synthetic data pipelines and systems that were adopted by others on your team and remain in use today.

• Experience owning the end-to-end cycle of identifying gaps in model usability and closing them through custom evaluations and training data.

• Experience making large-scale agentic RL infrastructure reliable given the long tail of failures that surface at scale.

• Experience improving the agentic capabilities of a frontier model.

• 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 $400,000 - $500,000 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.