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For Companies At a Glance
- Tasks: Run post-training pipelines and improve large language models for real-world applications.
- Company: Leading tech firm in London with a focus on innovative AI solutions.
- Benefits: Competitive salary up to £300k, equity options, and a dynamic work environment.
- Other info: Opportunity to work directly with product teams and shape the future of AI.
- Why this job: Join a small, high-performing team and make a significant impact in AI technology.
- Qualifications: Experience with LLMs, strong PyTorch skills, and a track record of impactful research.
Location: London, 5 days in office
Salary: Up to £300,000 + Equity
The role
Senior research engineer experienced in post‑training large language models, with a focus on scaling, RLHF, DPO, reward modelling, and fine‑tuning built on strong base models. The team is small, the bar is high, and the work ships into product used by enterprise customers.
The work
- Run post‑training pipelines and own model improvements end‑to‑end
- Build evaluation frameworks that distinguish real capability gains from benchmark theatre
- Debug training runs, reason about model behaviour, ship better weights
- Work directly with product on what to train for next
What you bring
- Direct experience post‑training or fine‑tuning LLMs at scale
- Strong PyTorch and distributed training fundamentals
- Published research, serious open‑source, or shipped production model work
Senior Research Engineer (LLM Post-Training £300k) in London employer: Dex
Contact Detail:
Dex Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Research Engineer (LLM Post-Training £300k) in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your work with LLMs, including any projects or research you've done. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common interview questions related to post-training models and be ready to discuss your past experiences in detail.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it makes it easier for us to keep track of your application and get back to you quickly.
We think you need these skills to ace Senior Research Engineer (LLM Post-Training £300k) in London
Some tips for your application 🫡
Show Off Your Experience: When you're writing your application, make sure to highlight your direct experience with post-training large language models. We want to see your skills in scaling, RLHF, and fine-tuning, so don’t hold back on the details!
Be Specific About Your Contributions: We love seeing how you've made an impact in your previous roles. Share specific examples of your work with PyTorch and distributed training, and if you’ve published research or contributed to open-source projects, let us know!
Tailor Your Application: Make your application stand out by tailoring it to our job description. Use the same language we do and connect your experiences directly to what we’re looking for in a Senior Research Engineer.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. We can’t wait to hear from you!
How to prepare for a job interview at Dex
✨Know Your Models Inside Out
Make sure you’re well-versed in the specifics of post-training large language models. Brush up on your experience with scaling, RLHF, DPO, and reward modelling. Be ready to discuss your past projects and how they relate to the role.
✨Showcase Your Problem-Solving Skills
Prepare to talk about how you've debugged training runs and improved model behaviour in previous roles. Use specific examples to illustrate your thought process and the impact of your solutions on model performance.
✨Understand the Product Connection
Since this role involves working directly with product teams, think about how your technical skills can translate into real-world applications. Be prepared to discuss what you would prioritise for training next based on customer needs.
✨Demonstrate Your Collaborative Spirit
With a small team and high expectations, showing that you can work well with others is crucial. Share experiences where you’ve collaborated effectively, especially in high-pressure situations, to highlight your teamwork skills.