Machine Learning Engineer - Now Hiring

• Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch

• Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration

• Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

• Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

• Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

• Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

• Implement methods from recent ML papers quickly and turn them into production-grade systems

• Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch

• Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration

• Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

• Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

• Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

• Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

• Implement methods from recent ML papers quickly and turn them into production-grade systems