Cursor The Model Engineer at Cursor will conduct research and engineering to improve large neural network training and reinforcement learning models. This role involves designing experiments and advancing training science for frontier-scale models.
Responsibilities
Design and run rigorous experiments that turn research ideas into measurable gains in model capability
Improve data recipes through data quality, deduplication, and mixture experiments
Define what makes RL data and rewards good, including grader design and preventing reward hacking
Advance training science, from architecture and scaling experiments to post-training recipes for coding and computer-use agents
Build evals and targeted tasks that expose model weaknesses and guide where the team invests next
Partner with engineers to take promising results from small experiments to frontier-scale training runs
Qualification
Truth seeking
Required
3+ years of hands-on experience training or post-training large neural networks (1B+ parameters), including work on pre-training, fine-tuning, or RL-based methods (e.g., RLHF, DPO).
Experience writing production-quality code in Python, PyTorch, JAX, Rust, or C++
Preferred
Able to handle ambiguous research tasks with little guidance, and to own a research direction end to end
Care an unusual amount about data quality, and can dive into the data when appropriate
Truth seeking, aiming to learn more about the science than to prove your ideas are correct
Experience with RL algorithms for language models, scaling laws, or building RL environments and evals
Experience with distributed training at scale and debugging problems that only show up at scale
Experience training frontier-scale models
Strong background in RL and machine learning fundamentals
An excellent programmer and software engineer, comfortable working across the research stack in Python and PyTorch or JAX
