Research Engineer

Adecco; Greater London; On-site; GBP 60,000 - 82,000; Full time

Job summary

Adecco is hiring for a global technology research organization to bring on Research Engineers for its Fundamental AI Research group in London. This 12-month contract embeds you as a full member of a frontier research team, tackling core AI challenges and building high‑stakes models.

The role requires Python and PyTorch, experience with large‑scale ML models, and a rigorous scientific approach. You will collaborate with researchers, publish findings where allowed, and contribute to production

Qualifications

• Hands-on experience in machine learning, recommendation systems, pattern recognition, data mining, or AI.
• Strong Python programming skills with practical ML experience.
• Experience building ML models at scale and querying LLMs.

Responsibilities

• Advance ML science and infrastructure by conducting cutting-edge research in ML systems and LLMs.
• Design reinforcement learning environments and synthetic data pipelines for multi-agent systems.
• Develop robust, safe sandboxes and execution pipelines for AI agents.
• Collaborate with researchers and cross-functional teams to define feature roadmaps.
• Publish findings or contribute to production systems as allowed by policy.

Skills

Python

ML experience

Education

Relevant degree in Computer Science, Computer Engineering, or a relevant technical discipline

Tools

PyTorch

Job description

Contract Duration: 12 months with potential for extension

Salary: £70,990 per year

About the Opportunity

An industry-leading global technology research organization is seeking Research Engineers to join their flagship Fundamental AI Research group. This group is committed to advancing the field of artificial intelligence by making foundational technical breakthroughs to better understand and interact with the world.

In this role, you will tackle core systems challenges to sustainably accelerate the reach toward human-level intelligence. Unlike traditional contract positions focused on narrow or isolated tasks, you will be deeply embedded as a full member of a frontier research team—working directly alongside junior and senior staff on cutting‑edge research projects, building high‑stakes models, and solving core AI problems at scale.

Key Responsibilities

• Advance ML Science & Infrastructure: Carry out cutting‑edge research to push the state of the art in machine learning systems, large language models (LLMs), and generative AI.
• Recursive Self-Improvement Focus: Design and build reinforcement learning environments, generate synthetic data, and research multi‑agent systems where AIs collaborate to build and refine next‑generation models.
• Robust Infrastructure Development: Architect and deploy functional, safe sandboxes, execution pipelines, and tools that enable complex AI agents to operate reliably.
• Literature & Experimentation: Stay current with academic research, code deliverables alongside the engineering team, and execute complex experiments using massive datasets and large‑scale AI models.
• Cross‑Functional Collaboration: Partner closely with researchers and cross‑functional teams to define feature roadmaps, synthesize technical requirements, and communicate research progress.
• Research Contributions: Contribute directly to research initiatives with the potential to publish findings and impact production systems, subject to organization policies.

Role Requirements & Qualifications

Minimum Qualifications:

• Experience: Sufficient years of hands‑on experience in machine learning, recommendation systems, pattern recognition, data mining, or artificial intelligence.
• Core Technical Stack: Advanced proficiency in Python and hands‑on experience with deep learning frameworks such as PyTorch.
• Large‑Scale Models: Experience developing ML models at scale, including programmatically querying LLMs, LLM post‑training, and running experiments over massive datasets.
• Scientific Rigor: Demonstrated ability to apply a scientific framework to debug complex agent traces, evaluate model behaviour, and translate technical insights into actionable recommendations.
• Education: Relevant degree in Computer Science, Computer Engineering, or a relevant technical discipline (or equivalent practical experience).

Preferred Qualifications:

• Direct experience in generative AI, multi‑agent systems, or LLM research.
• Advanced academic training or equivalent practical research experience in Machine Learning, AI, or a closely related quantitative field (e.g., Master’s level coursework, doctoral‑level research, or specialized laboratory experience)

What Makes This Role Unique?

• True Research Embedment: Function as an end‑to‑end research member rather than being restricted to isolated annotation or basic pipeline maintenance.
• Frontier Lab Access: Work directly on high‑stakes, cutting‑edge subject areas—such as recursive self‑improvement and autonomous AI agent architectures—at an unprecedented pace.
• High‑Impact Exposure: Gain experience navigating high‑velocity, ambiguous research environments while contributing to critical enterprise models and potentially earning publication credits.