At a Glance
- Tasks: Develop and optimise machine learning models for real-world applications.
- Company: Leading AI research firm in Central London with a focus on impactful projects.
- Benefits: Competitive salary, comprehensive health insurance, and pension plan.
- Other info: High ownership role with opportunities for professional growth and collaboration with top peers.
- Why this job: Join a small team to tackle unsolved problems and see your work in production quickly.
- Qualifications: 5+ years in ML engineering, strong Python skills, and experience with live model deployment.
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 Technology Engineer in City of London employer: DURLSTON PARTNERS
As a Research 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 an attractive compensation package, including a competitive salary and comprehensive benefits such as medical, dental, and life insurance, alongside a pension plan. With a focus on employee growth and a culture that encourages innovation, this role provides a unique opportunity to make significant contributions in a fast-paced environment.
StudySmarter Expert Advice🤫
We think this is how you could land Research 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 interviews by brushing up on your technical knowledge and problem-solving skills. We recommend doing mock interviews with friends or using online platforms to simulate the real deal.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Keep an eye on our job postings and get your application in – you never know where it might lead!
We think you need these skills to ace Research 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 this 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. We love seeing real-world results!
Tailor Your Application:Make sure your application speaks directly to the job description. Highlight your experience with post-training, fine-tuning, and evaluation frameworks. We appreciate when candidates connect their skills 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 experience with post-training, fine-tuning, and alignment techniques. Highlight specific examples where your interventions improved model performance in a live environment.
✨Demonstrate Your Problem-Solving Skills
Prepare to talk about challenges you've faced in ML engineering, especially around evaluation and failure diagnosis. Share concrete instances where you identified issues from first principles and how you resolved them, showcasing your analytical thinking.
✨Showcase Your Technical Expertise
Brush up on your Python skills and be prepared to discuss your experience with data pipelines, distributed training, and model versioning. They’ll want to know how you debugged non-converging training runs, so have a few examples ready to illustrate your technical prowess.
✨Emphasise Your Ownership and Impact
This role values high ownership, so be ready to discuss how you've taken charge of projects in the past. Talk about your production track record and how your contributions directly impacted model quality and performance, demonstrating your results-driven mindset.
