Director, Production RL Agents & Tooling

JDA Software; Paris; Hybrid; EUR 150,000 - 190,000; Full time

Job summary

Blue Yonder seeks a Director of Reinforcement Learning & Agentic Post-Training to steer how we train LLM-based agents that operate supply chain software, using tools, APIs, and human feedback.

You will build and guide a team of ML engineers, design RL environments, and define success metrics across planning, warehouse, transportation, and operations, balancing cost, latency, and reliability.

Qualifications

• Have led a team to ship LLM models trained with Reinforcement Learning, SFT, DPO, RLHF/RLAIF and other post-trained models in production.
• Have led a team to train models to use tools, call APIs, interact with software environments, or complete multi-step tasks.
• Have a strong machine learning engineering background and can credibly lead engineers because you have built systems like this yourself.

Responsibilities

• Lead the technical strategy for reinforcement learning, post-training, and tool-using LLM agents within the AI Studio.
• Build and manage a team of machine learning engineers working on agent training, RL environments, reward modeling, evaluation, data generation, and training infrastructure.
• Design environments where LLM agents learn to operate Blue Yonder software through APIs, tools, workflows, simulations, and human feedback.
• Develop training and evaluation systems for multi-step supply chain workflows across planning, warehouse management, transportation, commerce, and network operations.

Skills

Python

PyTorch

Reinforcement Learning

LLM post-training workflows

Tool integration

Production ML systems

Mentoring engineers

Strategy & design

Tools

NVIDIA Nemotron

NVIDIA NeMo

Megatron

vLLM

Ray

Job description

Blue Yonder seeks a Director of Reinforcement Learning & Agentic Post-Training to steer how we train LLM-based agents that operate supply chain software, using tools, APIs, and human feedback.

You will build and guide a team of ML engineers, design RL environments, and define success metrics across planning, warehouse, transportation, and operations, balancing cost, latency, and reliability.