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- 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
- 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
- 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
- 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 City of London employer: BioTalent
Contact Detail:
BioTalent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Engineer in City of London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. 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
Prepare for those interviews by practising common questions and showcasing your projects. We recommend doing mock interviews with friends or using online platforms to get comfortable talking about your experience and skills.
✨Tip Number 3
Showcase your passion for machine learning! Share your insights on recent advancements or contribute to discussions in relevant forums. This not only demonstrates your knowledge but also helps you stand out as a candidate who’s genuinely interested in the field.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it’s super easy to keep track of your applications this way!
We think you need these skills to ace Research Engineer in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Research Engineer role. Highlight your hands-on experience with post-training techniques and any relevant projects you've worked on. We want to see how you can bring value to our team!
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 your background aligns with our mission at StudySmarter. Don’t forget to mention specific examples of your work that relate to the job description.
Showcase Your Projects: If you've got any personal or open-source projects, make sure to include them in your application. We love seeing practical applications of your skills, especially if they involve post-training techniques or ML models. It gives us a glimpse into your hands-on experience!
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It streamlines the process for us and ensures your application lands in the right hands. Plus, it shows you're keen on joining our team!
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 how you've applied these methods in past projects, as this will show your hands-on experience and technical depth.
✨Showcase Your Experimentation Skills
Prepare to talk about your experience designing and executing experiments. Bring examples of how you tracked metrics and drew conclusions from your findings. This will demonstrate your ability to lead experiments end-to-end, which is crucial for the role.
✨Communicate Clearly with Technical Trade-offs
Practice explaining complex technical concepts in simple terms. You’ll need to collaborate with various teams, so being able to articulate your thought process and decisions clearly will be key to your success in the interview.
✨Stay Updated on ML Research
Familiarise yourself with the latest advancements in post-training research. Being able to discuss recent developments and how they could apply to the company’s work will show your enthusiasm and commitment to the field.
