Machine Learning Engineer

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