Russell Tobin
Machine Learning Engineer
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Software Engineer (AI Research / Machine Learning)
Duration: 6 Months (Extension possible) Location: London – Hybrid, 3 days onsite per week Working Hours: Monday to Friday, 9:00 AM – 6:00 PM (40 hours per week)
About the Role
Our client is seeking Research Engineers to join their Fundamental AI Research team. The team is committed to advancing artificial intelligence through fundamental research and developing technologies that help machines better understand and interact with the world.
This is an opportunity to work on cutting-edge AI research, building large language model (LLM) agents, and solving complex machine learning systems challenges at scale.
Responsibilities
- Carry out cutting-edge research to advance the science and technology of machine learning systems.
- Design methods, tools, and infrastructure to push forward the state of the art in large language models (LLM).
- Collaborate with researchers and cross-functional partners, communicating research plans, progress, and results.
- Suggest, collect, and synthesize requirements and create an effective feature roadmap.
- Code deliverables alongside the engineering team.
- Publish research results and contribute to research that impacts product development.
Minimum Qualifications
- 2+ years of hands-on experience in one or more of the following:
- Machine Learning
- Recommendation Systems
- Pattern Recognition
- Data Mining
- Artificial Intelligence
- Experience developing machine learning models at scale, including programmatically querying LLMs and LLM post-training.
- Strong programming experience in Python.
- Hands-on experience with PyTorch or similar frameworks.
- Experience writing software and executing complex experiments involving large AI models and datasets.
- Proven ability to translate insights into business recommendations.
- Bachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
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Preferred Qualifications
- Direct experience in Generative AI and LLM research.
- PhD or research-focused Master's degree in Machine Learning or related fields.
- Project Overview
The successful candidate will join an AI research team focused on:
- Building machine learning agents.
- Creating AI systems that automate research workflows.
- Developing LLM agents.
- Working across multiple streams of the same research project.
- Writing production-quality code using Python and PyTorch.
Team
Part of a leading AI research organisation based in London.
- Working closely with research engineers, research scientists, and other contingent workers.
- Collaborative, fast-paced research environment.
Top 3 Non-Negotiable Skills
- Strong scientific mindset – organised, diligent, and analytical.
- Excellent engineering skills with the ability to execute and deliver quickly.
- Ability to work efficiently in a high-output coding environment.
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Nice to Have
- Students or graduates from leading universities.
- Highly motivated with a passion for learning.
- Experience with leading technology companies (including internships).
- Background in Physics or Mathematics.
- Experience as an Applied Scientist, Research Engineer, or similar.
- Experience with Python, PyTorch, LLM agents, pre-training, or broader machine learning disciplines.
- Experience with quantum computing is a plus.
Experience Required
2–4 years (Industry: 0–4 years).
- Strong academic or internship exposure to Machine Learning, AI, research, or startups will also be considered.
What Makes This Role Interesting
- Opportunity to work on cutting-edge AI research.
- Contribute to technology that has the potential to change the world.
- Work alongside some of the leading researchers in the field.
- One of the few opportunities of this kind currently available in London.
Candidate Profile
Successful candidates will demonstrate:
- Passion for machine learning and AI research.
- Strong execution mindset.
- Ability to work collaboratively in a team environment.
- Adaptability and willingness to work in a fast-moving research setting.
Challenges
Candidates should be comfortable with:
- Working in a high-pressure environment.
- Moving across different workstreams within the team.
- Handling ambiguity and rapidly evolving priorities.
Interview Process
- Initial Screening Interview
- Two Loop Interviews
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