Machine Learning Engineer - Reinforcement Learning

Responsibilities:

• Design and implement LLM-powered agent environments for supply chain decision-making
• Fine-tune, adapt, and evaluate LLMs for domain-specific reasoning and decision support
• Design, test, and iterate on reward functions that capture the behaviors we want from LLM agents
• Review LLM traces and rollouts to understand model reasoning, failure modes, reward hacking, and shortcut behaviour
• Identify when an LLM is exploiting the reward function, escaping the intended RL process, or optimizing for proxy metrics instead of the real objective
• Improve reward models, environment design, prompts, tools, and feedback loops based on observed model behaviour
• Build evaluation frameworks to measure model quality, agent performance, robustness, and failure modes
• Create data pipelines for training, fine-tuning, preference data, synthetic data generation, and human feedback collection
• Develop tooling that improves how the team builds, tests, debugs, and deploys AI-assisted workflows
• Experiment with RL, RLHF, RLAIF, reward shaping, policy optimization, and agent training techniques
• Document what works, what fails, and why, so we can compound our learnings over time
• Stay close to the frontier of LLMs, agents, evaluations, and applied AI engineering
• We want to talk if you:
• You've trained or fine-tuned LLMs
• Are excited about AI-assisted tools and getting the most out of them
• Build & customize your own AI workflows
• Have experience working with AI agents and RL environments in production
• Are proficient in Python and PyTorch
• Can balance research exploration with shipping working code
• Hands on experience with RL techniques (reward shaping, policy optimization, RLHF)
• Thrive in fast-moving environments where priorities shift
• Care about craft in your work
• Are curious about why things work, not just that they work
• Bonus points if:
• You have experience with human-in-the-loop ML systems
• You've built evaluation frameworks for open-ended tasks
• You're familiar with supply chain, logistics, or operations domains
• You have a side project that shows you can't stop tinkering

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