Machine Learning Engineer - Agentic AI & Reinforcement Learning

Viridien () is an advanced technology, digital and Earth data company that pushes the boundaries of science for a more prosperous and sustainable future. With our ingenuity, drive and deep curiosity we discover new insights, innovations, and solutions that efficiently and responsibly resolve complex natural resource, digital, energy transition and infrastructure challenges.

Are you interested in working on real world data like no other?

Viridien is expanding its capabilities in agentic AI, large language models and reinforcement learning for complex scientific and enterprise workflows.

We are looking for a hands-on Machine Learning Engineer to design, develop and evaluate intelligent agent systems, turning emerging AI methods into reliable, scalable and reusable solutions for truly real world data.

About the Team

Learn more about our work and the team on our website, recent blog post featuring one of our senior machine learning engineers and our latest Voices of Viridien.

Key responsibilities

• Design and develop single-agent and multi-agent systems for complex workflows.
• Build reasoning, planning, task decomposition, memory, tool-use and agent-collaboration capabilities.
• Integrate LLMs, vision-language models, retrieval systems and domain-specific tools.
• Fine-tune and adapt foundation models using supervised learning, preference optimisation and reinforcement learning where appropriate.
• Develop training environments, reward functions, graders and feedback mechanisms.
• Build evaluation, guardrail and monitoring approaches for agent reliability.
• Convert prototypes into production-ready APIs, services and reusable software components.

Experience and skillsRequired

• Degree in computer science, artificial intelligence, machine learning, engineering or a related discipline, or equivalent practical experience.
• Strong Python programming and software-engineering skills.
• Experience developing machine-learning, generative AI or LLM applications.
• Practical experience with PyTorch, JAX, TensorFlow or a similar framework.
• Understanding of agentic AI concepts such as tool calling, planning, memory and workflow orchestration.
• Knowledge of reinforcement learning, preference optimisation or sequential decision-making.
• Experience designing model evaluations and translating research ideas into reliable software.

Desirable

• Experience developing multi-agent systems using LangGraph, AutoGen, smolagents or similar frameworks.
• Experience with LLM fine-tuning, DPO, GRPO, PPO, RLHF or RLAIF.
• Experience designing reward functions, graders or agent training environments.