Connectionism: Research Blog by Thinking Machines Lab. Backed by a16z, Accel and GV.
About the role
We’re looking for a researcher to help develop reliable model evaluations for research signals. This role spans evaluation creation, usability, auditing, and efficiency. You’ll work closely with researchers and engineers across post-training and the broader research organization. Depending on your interests and experience, you may focus on one area or work across several of these problems.
What they're looking for
- Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding
- Experience designing, building, or analyzing evaluations, benchmarks, datasets, graders, or other measurement systems
- Strong written and verbal communication skills, with the ability to collaborate effectively across research and engineering teams
- Preferred qualifications — we encourage you to apply if you meet some but not all of these:
- Experience with LLMs, post-training, reinforcement learning, or agentic systems
- Experience with evaluation auditing, human evaluations, LLM-judges, or open-ended task evaluation
More about this role
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.
We’re looking for a researcher to help develop reliable model evaluations for research signals. This role spans evaluation creation, usability, auditing, and efficiency.
You’ll work closely with researchers and engineers across post-training and the broader research organization. Depending on your interests and experience, you may focus on one area or work across several of these problems.
Create internal evaluations and research signals for capabilities and behaviors important to model research and post-training.
Develop usability evaluations that measure whether models are genuinely useful in real research and product workflows, and partner with the data flywheel to turn evaluation insights into better data and training signals.
Improve evaluation robustness , including grader reliability, ambiguous ground truth, evaluator disagreement, false...
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