Thinking Machines Lab · 1 month ago
Research, Post-Training Data
Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. The role of post-training researchers is critical in bridging raw model intelligence with practical, safe, and collaborative AI systems, focusing on human insight and machine learning.
Artificial Intelligence (AI)Foundational AIGenerative AIInformation TechnologyProduct ResearchSoftware
Responsibilities
Design and execute data collection and synthesis strategies for post-training by combining human feedback, preference data, and synthetic examples to guide model behavior
Develop pipelines and frameworks for scalable, high-quality human labeling, model-assisted labeling, and synthetic data generation
Research and model human preferences and behavior, creating data-driven methods to improve reasoning, truthfulness, and helpfulness
Iterate on evals: post-training involves a never-ending loop of defining a set of evaluations, optimizing them, and then realizing your existing evals don’t capture what matters. You’ll be responsible for both making numbers go up, and making sure the numbers are meaningful
Design and evaluate metrics and benchmarks that measure data quality, alignment, and the real-world impact of post-training interventions
Scale and explore: post-training will involve a combination of scaling the existing methodologies and developing new ones
Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia
Qualification
Required
Strong engineering skills, ability to contribute code and debug in complex codebases
Experience with data curation, human feedback, or synthetic data generation for large language models or similar systems
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
Preferred
A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs
Prior experience with RLHF, RLAIF, preference modeling, or reward learning for large models
Experience managing or analyzing human data collection campaigns or large-scale annotation workflows
Research or engineering contributions in alignment, data-centric AI, or human-AI collaboration
Familiarity with synthetic data pipelines, active learning, or model-assisted labeling
PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience
Benefits
Generous health, dental, and vision benefits
Unlimited PTO
Paid parental leave
Relocation support as needed
Company
Thinking Machines Lab
Thinking Machines Lab is an AI research and product company that aims to increase understanding and customization of AI systems.
H1B Sponsorship
Thinking Machines Lab has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (9)
Funding
Current Stage
Early StageTotal Funding
$2.01BKey Investors
Andreessen HorowitzMinistry of Economy, Culture and Innovation
2025-06-20Seed· $2B
2025-05-05Grant· $9.98M
Recent News
Indian Express
2026-01-18
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