Machine Learning Engineer - Agentic AI & Reinforcement Learning
CGG (Compagnie Générale de Géophysique)2 hours ago
Crawley, United KingdomMid Level
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.
Requirements
- 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 including 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.
- Experience developing multi-agent systems with LangGraph, AutoGen, smolagents, or similar frameworks is desirable.
- Experience with LLM fine-tuning, DPO, GRPO, PPO, RLHF, or RLAIF is desirable.
- Experience designing reward functions, graders, or agent training environments is desirable.
- Experience with Model Context Protocol and tool integration is desirable.
- Experience deploying open-source language or vision-language models, including distributed training or GPU computing, is desirable.
- Experience applying AI to scientific, engineering, energy, or geoscience workflows is desirable.
Benefits
- Competitive salary commensurate with experience and a highly attractive bonus scheme.
- Initial 22 days of annual leave with future increases, plus flexible holiday buying and selling programs.
- Hybrid working with two days at home and flexible working.
- Company pension with generous employer contribution.
- Unmind mental-health and wellbeing app.
- Flexible benefits platform with discounts including gym memberships, restaurants, and cinema tickets.
- Cycle purchase scheme.
- Flexible private medical and dental care programs.
- Bank Holiday Swap program.
- Relaxed dress code policy.
- Tailored technical, commercial, and personal development training through the Learning Hub.
- Community volunteering and environmental sustainability initiatives.
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