Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Own the lifecycle of ML models, from adaptation to deployment.
- Company: Fast-growing tech company focused on advanced machine learning systems.
- Benefits: Competitive pay, long-term growth opportunities, and high autonomy.
- Why this job: Make a real impact by translating research into practical ML applications.
- Qualifications: MSc or PhD in Machine Learning or related field; strong Python and PyTorch skills.
- Other info: Collaborate with experts in a dynamic, engineering-led environment.
The predicted salary is between 36000 - 60000 £ per year.
This role is within a fast-growing technology company developing advanced machine learning systems for real-world, data-intensive applications. The organisation focuses on adapting general-purpose foundation models to highly specific contexts, delivering high-value, production-ready solutions at scale.
As an Applied Research Engineer, you will own the end-to-end lifecycle of post-training ML models, from adaptation and validation to deployment. You will make core technical decisions that determine model performance and usability in real-world settings, collaborating closely with ML infrastructure and domain teams.
This Will Offer You
- Ownership of the full post-training workflow for foundation models in production environments
- Responsibility for translating research into practical, high-impact ML applications
- Close collaboration with engineering, product, and domain experts
- Exposure to large-scale model adaptation, distributed training, and experimental design
- A high-autonomy role in a fast-paced, engineering-led environment
- Competitive compensation and long-term growth opportunities
Your Responsibilities
- Design and implement post-training pipelines to adapt foundation models to specific use cases
- Build validation frameworks that link model improvements to real-world or domain-specific metrics
- Lead experiments end-to-end: from hypothesis through distributed training runs to analysis and deployment
- Collaborate with ML engineers and domain experts to ensure outputs are scientifically or technically meaningful
- Contribute to internal and open-source tooling, helping shape the technical direction of post-training capabilities
- Keep up-to-date with post-training research and integrate relevant advances into production
You Will Bring
- MSc or PhD in Machine Learning, Computational Biology, or a related technical field, or equivalent experience
- Hands-on experience with post-training techniques such as fine-tuning, LoRA, DPO, RLHF, or similar alignment methods
- Strong Python and PyTorch skills; comfortable with training loops, distributed runs, and model internals
- Familiarity with modern ML architectures, particularly Transformers
- Experience designing and executing experiments rigorously, tracking metrics, and drawing valid conclusions
- Ability to work autonomously and make decisions with incomplete information
- Strong communication skills to explain technical trade-offs across teams
Nice to have:
- Experience with foundation models in a scientific or technical domain
- Familiarity with distributed multi-GPU or multi-node training frameworks
- Contributions to open-source ML tooling or relevant publications
- Experience integrating post-training improvements into production systems
Research Engineer in London employer: BioTalent
Contact Detail:
BioTalent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Engineer in London
✨Tip Number 1
Network like a pro! Reach out to 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 help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects and experiments. We want to see your hands-on experience with post-training techniques and how you've tackled real-world problems.
✨Tip Number 3
Prepare for those interviews! Brush up on your Python and PyTorch skills, and be ready to discuss your approach to model adaptation and validation. We’re looking for candidates who can communicate their thought process clearly.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Research Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Research Engineer role. Highlight your experience with post-training techniques and any relevant projects you've worked on. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how you can contribute to our team. Be specific about your experiences and how they relate to the job description.
Showcase Your Projects: If you've worked on any interesting ML projects, make sure to mention them! Whether it's a personal project or something from your studies, we love seeing practical applications of your skills. Include links if possible!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at BioTalent
✨Know Your Models Inside Out
Make sure you’re well-versed in the post-training techniques mentioned in the job description, like fine-tuning and RLHF. Be ready to discuss your hands-on experience with these methods and how they can be applied to real-world scenarios.
✨Showcase Your Experimentation Skills
Prepare to talk about your past experiments, focusing on how you designed them, tracked metrics, and drew conclusions. Highlight any specific examples where your work led to significant improvements in model performance.
✨Communicate Clearly and Confidently
Since collaboration is key in this role, practice explaining complex technical concepts in simple terms. Think about how you would communicate trade-offs to non-technical team members and be ready to demonstrate your strong communication skills.
✨Stay Updated on ML Trends
Research the latest advancements in post-training techniques and be prepared to discuss how you would integrate these into production systems. Showing that you’re proactive about keeping up with industry trends will impress your interviewers.
