At a Glance
- Tasks: Drive cutting-edge ML research and engineering with real-world impact.
- Company: Innovative AI firm in Central London with a focus on practical solutions.
- Benefits: Competitive salary, comprehensive health insurance, and pension plan.
- Other info: High ownership role with opportunities for significant career growth.
- Why this job: Join a small team to solve unsolved problems and see results in weeks.
- Qualifications: 5+ years in ML engineering with hands-on production experience.
Most AI research roles sit at one of two extremes - pure academia with little real-world impact, or pure engineering with little room to think. This one sits in the narrow band between them, where the work is serious, the models are live, and the problems are genuinely unsolved.
You'll work on the core model stack - post-training, fine-tuning, alignment, evaluation - and see the results in production within weeks, not quarters. The measure of success is model performance in the real world, not benchmark scores or citation counts.
Small team. High ownership. High-calibre peers.
WHAT YOU'LL WORK ON
- Post-training & alignment: Designing and running SFT pipelines and applying alignment techniques - RLHF via PPO or the more direct DPO family. You understand what each approach optimises for, where each breaks down, and which to reach for given the data and objective at hand.
- Parameter-efficient fine-tuning: Production experience with LoRA and QLoRA. You understand how rank, the alpha-to-rank scaling ratio, and target module selection interact with model behaviour.
- Evaluation & failure diagnosis: Building eval frameworks tied to real-world outcomes. You can identify why a model is failing from first principles.
- Training infrastructure: Owning data pipelines, distributed training runs, and model versioning end-to-end. PyTorch is your default. You've debugged a training run that wasn't converging and knew where to look.
WHAT WE NEED TO SEE
- You have taken an LLM through post-training and into a live production environment.
- You can describe a specific model behaviour you improved, what you changed, and why it worked.
- 5+ years in ML engineering or applied research with a clear production track record.
- Deep Python across data, training, evaluation and serving - no significant gaps.
- Strong academic backgrounds, provided they come with at least two years of hands-on production ML experience post-research. This is not the right first step after a PhD or from a research lab.
WHO TENDS TO BE A STRONG FIT
Senior ML engineers who have owned model quality end-to-end - the performance obsession, rigour under pressure, and instinct to optimise rather than theorise translate directly.
COMPENSATION & BENEFITS
- £220,000 – £280,000 base salary
- Medical, dental and life insurance
- Pension plan
Reach out directly if the above describes you at dana@durlstonpartners.com
Research and Technology Engineer in City of London employer: DURLSTON PARTNERS
As a Research and Technology Engineer in Central London, you'll join a dynamic team that values high ownership and collaboration, working on cutting-edge AI models with real-world impact. The company offers competitive compensation, comprehensive benefits including medical and dental insurance, and a strong focus on employee growth through hands-on experience in a fast-paced environment. With a culture that encourages innovation and problem-solving, this role is perfect for those looking to make a meaningful contribution in the field of machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land Research and Technology Engineer in City of London
✨Tip Number 1
Network like a pro! Connect with folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get you in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects and contributions to ML. We want to see your work in action, so make it easy for potential employers to see what you can do.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your past experiences in detail. We recommend practising common ML scenarios and problem-solving questions to impress your interviewers.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. We’re always on the lookout for talented individuals who fit the bill, so don’t hesitate to put yourself out there!
We think you need these skills to ace Research and Technology Engineer in City of London
Some tips for your application 🫡
Show Your Passion for ML:When you're writing your application, let your enthusiasm for machine learning shine through. We want to see that you’re not just ticking boxes but genuinely excited about the challenges and innovations in the field.
Be Specific About Your Experience:Don’t just list your past roles; dive into the details! Share specific examples of models you've worked on, how you improved their performance, and the impact of your contributions. This helps us understand your hands-on experience.
Tailor Your Application:Make sure your application speaks directly to the job description. Highlight your skills in post-training, fine-tuning, and evaluation frameworks. We love seeing candidates who can connect their experience to what we need!
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications better and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at DURLSTON PARTNERS
✨Know Your Models Inside Out
Make sure you can discuss the models you've worked on in detail. Be ready to explain your post-training processes, alignment techniques, and how you've improved model behaviour. This role is all about real-world impact, so focus on specific examples where your work made a difference.
✨Demonstrate Your Production Experience
Highlight your hands-on experience with ML engineering in production environments. Talk about the challenges you faced, like debugging non-converging training runs, and how you overcame them. This will show that you have the practical skills needed for this role.
✨Showcase Your Evaluation Skills
Be prepared to discuss how you've built evaluation frameworks tied to real-world outcomes. Explain your approach to diagnosing model failures from first principles. This will demonstrate your analytical thinking and problem-solving abilities, which are crucial for success in this position.
✨Emphasise Team Collaboration
Since this role involves working in a small team with high ownership, share examples of how you've collaborated with peers in the past. Discuss how you’ve contributed to team success and how you handle feedback. This will highlight your ability to thrive in a dynamic environment.
