Software Engineer (Full-Stack)

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

About

This role is about making models better after their initial training. You'll work on reinforcement learning and other post-training methods, while also improving the systems needed to run those experiments efficiently at scale.

The company is building AI systems that learn how to carry out complex work inside large organisations. They recreate real-world workflows as interactive training environments, then use those environments to train models through practice and feedback — so the models get better at completing long, multi-step tasks reliably, rather than simply generating answers.

You'll work across both the learning algorithms and the infrastructure underneath them. That means going from an RL experiment to a GPU profiler trace, finding what's limiting performance, and making sure systems improvements don't change the way the model learns.

What you'll do

• 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

What you'll need

• 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 comfortably across ML research and systems engineering

Optional

• vLLM, SGLang, Ray, FSDP or Megatron-LM
• CUDA, Triton, CuTE or GPU performance optimisation

Shortlisted candidates will be contacted within 48 hours.