ML Systems Engineer: RL & Scalable Training

Axiōma Search; San Francisco, New York (CA, NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Axiōma Search is seeking a skilled ML/AI engineer to advance post-training methods and reinforcement learning. You will bridge ML research and systems engineering to optimize training workflows and scale across GPU clusters.

The role focuses on improving supervised fine-tuning and RL approaches, diagnosing performance bottlenecks, and ensuring robust model updates from rollout to checkpointing in distributed environments.

Qualifications

• Strong programming and quantitative problem-solving skills.
• Hands-on experience with PyTorch and model training.
• Understanding of reinforcement learning or LLM post-training.
• Experience with distributed training and GPU systems.
• Strong experimental judgement.
• Ability to work across ML research and systems engineering.

Responsibilities

• Build and improve supervised fine-tuning, preference optimisation and RL methods.
• Work with approaches including PPO, GRPO and SDPO.
• Own training loops from rollout generation through to policy updates and checkpointing.
• Improve training throughput, GPU utilisation and memory efficiency.
• Profile and fix bottlenecks across distributed training.
• Investigate instability and differences between training and inference.
• Use real model failures to improve rewards, training data and overall performance.

Skills

PyTorch

Reinforcement learning

Distributed training

Experimentation

Research to systems

Tools

Ray

Megatron-LM

Job description

Axiōma Search is seeking a skilled ML/AI engineer to advance post-training methods and reinforcement learning. You will bridge ML research and systems engineering to optimize training workflows and scale across GPU clusters.

The role focuses on improving supervised fine-tuning and RL approaches, diagnosing performance bottlenecks, and ensuring robust model updates from rollout to checkpointing in distributed environments.