1 open role mentioning Low-precision numerics
Thinking Machines Lab
Thinking Machines is seeking a researcher to scale reinforcement learning for frontier models. This role focuses on the intersection of training recipes and infrastructure, particularly for high-training-compute, long-horizon RL. A key area of interest is asynchronous RL, requiring a deep understanding of its algorithms, design choices, and trade-offs on both ML and systems sides. The position also emphasizes the importance of inference systems, low-precision numerics, and quantization, as these significantly impact RL scaling and cost. This is a research role with full-stack ownership, from algorithms to training run health.
$350k - $475k