Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Build and deploy cutting-edge AI models to solve complex problems.
- Company: Well-funded AI startup with a team of top-tier talent.
- Benefits: Competitive salary up to £300k, significant equity, and a dynamic work environment.
- Why this job: Tackle genuine research challenges and make an impact in AI production.
- Qualifications: 5+ years in AI/ML engineering, strong Python skills, and hands-on experience with LLMs.
- Other info: Collaborate with elite engineers in a vibrant London office.
Our client is a very well-funded AI startup solving some of the hardest problems in production large language models. They are building systems that reliably handle complex, multi-step reasoning tasks at massive scale.
Their team combines talent from top-tier quantitative trading firms, leading AI research labs, and Big Tech. Backed by tier-one venture capital, they ship fast, maintain exceptionally high technical standards, and operate as an in-office team in London.
They are hiring a Senior Research Engineer to build and deploy core AI models from conception through production. You will work on genuinely hard, unsolved problems: building LLMs that reason reliably across long contexts, maintain coherent state over extended interactions, and make sound decisions under uncertainty – all whilst serving millions of requests with strict latency requirements.
- Novel post-training methods – Training approaches optimised for real-world task completion, not benchmarks
- Real-time evaluation and orchestration – Systems that monitor quality and adaptively route between models in production
- Long-horizon task decomposition – AI systems that autonomously break down and execute multi-step problems
Requirements:
- 5+ years AI/ML engineering or research experience with production systems
- Hands-on post-training experience (RLHF, DPO, or similar) and deployed LLMs in production
- Strong Python and modern ML tooling (PyTorch/JAX, training infrastructure, evaluation pipelines)
- Track record shipping research from prototype to production impact
- Comfortable owning work end-to-end: architecture to data pipelines to production integration
Work on genuine research problems that ship to production within weeks. Collaborate with exceptional engineers from elite quant firms, frontier AI labs, and top tech companies. Own multi-quarter initiatives and shape the research roadmap. In-office in London with some of Europe’s best AI talent.
For more information, or a discreet chat regarding the market/similar roles, please get in touch:
Research Technology Engineer in London employer: Durlston Partners
Contact Detail:
Durlston Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Technology Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and tech scene, especially those who work at startups or have connections to them. Attend meetups, webinars, or even just grab a coffee with someone in the industry – you never know where a casual chat might lead!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to AI/ML. If you've built or deployed models, share your experiences and results. This will help you stand out and demonstrate your hands-on expertise.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and ML tools. Practice coding challenges and be ready to discuss your past projects in detail. We want to see how you think and solve problems, so be ready to dive deep into your experience!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team and tackling those challenging AI problems together!
We think you need these skills to ace Research Technology Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Senior Research Engineer. Highlight your experience with AI/ML engineering, especially any hands-on post-training work you've done. We want to see how your skills align with the challenges we face in production systems.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about solving complex problems in AI and how your background makes you a perfect fit for our team. We love seeing genuine enthusiasm and insight into your thought process.
Showcase Your Projects: If you've worked on any relevant projects, make sure to include them! Whether it's deploying LLMs or developing novel training methods, we want to see what you've accomplished. Include links or descriptions that demonstrate your impact and technical prowess.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us that you're proactive and genuinely interested in joining our team!
How to prepare for a job interview at Durlston Partners
✨Know Your Stuff
Make sure you brush up on your AI/ML engineering knowledge, especially around production systems and large language models. Be ready to discuss your hands-on experience with post-training methods like RLHF or DPO, and how you've deployed LLMs in real-world scenarios.
✨Showcase Your Projects
Prepare to talk about specific projects where you've taken research from prototype to production. Highlight the challenges you faced and how you overcame them, especially in terms of architecture, data pipelines, and integration.
✨Be Ready for Technical Questions
Expect some deep technical questions related to Python, PyTorch, JAX, and modern ML tooling. Brush up on your knowledge of evaluation pipelines and be prepared to explain your thought process clearly and confidently.
✨Demonstrate Collaboration Skills
Since this role involves working with top-tier talent, be ready to discuss how you've collaborated with others in past roles. Share examples of how you’ve contributed to team initiatives and shaped research roadmaps, showing that you can own multi-quarter projects.
