Connectionism: Research Blog by Thinking Machines Lab. Backed by a16z, Accel and GV.
About the role
As a safety researcher, you'll work toward ensuring our models are safe and trustworthy. The role sits at the intersection of research and hands-on technical work. A central question is how models come to handle harmful or dual-use requests: what they learn from data, how training shapes where they refuse and where they engage, and what makes those boundaries reliable. You'll explore the science behind these behaviors and design experiments that inform how our models are trained and evaluated.
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
- Background in AI safety research, with hands-on experience in at least one area of safety, such as: RLHF/RLAIF, alignment and preference modeling, deliberative alignment, safety evaluations, or red-teaming
- Proficiency in Python and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX). Comfort debugging distributed training and writing code that scales
- Clarity in communication, an ability to explain complex technical concepts in writing
- Preferred qualifications — we encourage you to apply if you meet some but not all of these:
- Experience building evaluations for long-horizon, multi-step, or agentic tasks
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.
As a safety researcher, you'll work toward ensuring our models are safe and trustworthy. The role sits at the intersection of research and hands-on technical work. A central question is how models come to handle harmful or dual-use requests: what they learn from data, how training shapes where they refuse and where they engage, and what makes those boundaries reliable. You'll explore the science behind these behaviors and design experiments that inform how our models are trained and evaluated.
We are hiring across the entire development stack — from pre-training data curation to safety-focused fine-tuning, evaluations, and red-teaming. During project selection we’ll take into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented safety researchers with the teams where they'll have...
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