Machine Learning Engineer - Reinforcement Learning

The AI Studio's mission is to find the fastest possible path to an autonomous supply chain. We're developing AI agents, learning systems, training models, and more to overcome the biggest challenges remaining in the global supply chain.

In short, we are having a lot of fun.

Your Mission In This Role

We’re looking for an ambitious ML Engineer focused on LLMs, agents, and reinforcement learning to help build the training, evaluation, and tooling systems behind robust AI decision-making products.

You’ll work across LLM fine-tuning, agent environments, reward modeling, evaluations, data pipelines, and AI workflow tooling. The role is hands-on: designing experiments, shipping production code, improving model behaviour, and building the infrastructure that lets us learn quickly from both automated and human feedback.

You’ll help shape how we use LLMs inside agentic systems, how we evaluate model and agent performance, and how we turn feedback into better training data and better behaviour.

This role requires mandatory  RL training experience with LLMs, including designing and iterating on rewards, reviewing LLM traces, identifying reward hacking or shortcut behaviour, and understanding when the reward signal, environment, or training process needs to change.

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 Py

Torch

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#LI-HG1Our Values

If you want to know the heart of a company, take a look at their values. Ours unite us. They are what drive our success – and the success of our customers. Does your heart beat like ours? Find out here: Core Values

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran : Paris

Type: Full time