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- Tasks: Transform research-driven ML prototypes into scalable platforms and automate scientific workflows.
- Company: Global IT consultancy at the forefront of pharma and biotech innovation.
- Benefits: Flexible day rate, dynamic work environment, and opportunities for professional growth.
- Why this job: Make a real impact in drug discovery and biological data analysis with cutting-edge technology.
- Qualifications: Experience in pharma, biotech, or life sciences; strong Python and ML engineering skills required.
- Other info: Work onsite in King's Cross, London, and collaborate with top scientists and engineers.
The predicted salary is between 50000 - 70000 £ per year.
Inside IR35 | Contract | 3 Days Onsite (King's Cross, London)
Day Rate: Flexible (DOE)
Pharma / Biotech / Life Sciences / Bioinformatics
To be eligible for the role, you must have a valid working visa (e.g., ILR, British citizenship, EU passport).
A global IT consultancy is seeking a highly skilled AI/ML Engineer to help transform research-driven machine-learning prototypes into scalable, production-ready platforms. This role sits at the intersection of ML engineering, scientific computing, and cloud infrastructure, supporting advanced R&D, drug discovery, and biological data analysis.
Non-Negotiable Requirement
You must have real, professional experience in pharma, biotech, life sciences, or bioinformatics, due to the close integration with scientific research.
Role Overview
You will work alongside data scientists, computational biologists, and engineering teams to build reliable ML workflows, automate experimentation, and improve overall MLOps maturity. This is an Inside IR35 contract requiring 3 days onsite each week in London – King’s Cross. The day rate is fully flexible depending on experience.
Key Responsibilities
- Convert notebook-based scientific experiments into production-ready ML pipelines
- Containerise, optimise, and deploy ML/LLM models
- Work with complex scientific datasets (omics, assay, imaging, molecular, clinical, etc.)
- Build automated ML workflows including training, evaluation, and monitoring
- Implement MLOps best practices: CI/CD, model versioning, reproducibility, scalable orchestration
- Collaborate with domain scientists to raise engineering standards
- Contribute to core scientific AI platforms and internal ML tooling
Required Skills & Experience
- Strong production-level Python for ML engineering
- Experience with modern ML tooling (Databricks, Ray, Kubernetes, MLflow, ClearML, Weights & Biases)
- Hands-on deployment experience on AWS and Azure (SageMaker, EKS, AML, AKS)
- Proven experience working with large-scale scientific datasets common in pharma/biotech environments
- Practical experience with LLMs, generative AI, or modern deep learning architectures
- Strong engineering fundamentals: CI/CD, Git, testing, IaC, containers
Preferred (Nice to Have)
- Experience with HPC or GPU-accelerated ML workloads
- Familiarity with scientific libraries such as BioPython, RDKit, Scanpy
- Exposure to regulatory or compliance aspects of drug discovery or clinical research
- Prior experience supporting scientific teams in R&D settings
Ideal Candidate
- A hybrid ML engineer who bridges scientific research and production
- Strong communicator, comfortable partnering with scientific stakeholders
- Proactive, organised, and effective in large-scale enterprise R&D environments
AI/ML Engineer - Scientific & Research Platforms in London employer: Nicoll Curtin Technology
Contact Detail:
Nicoll Curtin Technology Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI/ML Engineer - Scientific & Research Platforms in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the pharma and biotech sectors. Attend meetups or webinars related to AI/ML in life sciences. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those relevant to scientific research. This can be a game-changer during interviews, as it gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and ML tools. Practice coding challenges and be ready to discuss your experience with large-scale datasets. Confidence in your technical abilities can really set you apart!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that match your skills. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace AI/ML Engineer - Scientific & Research Platforms in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the AI/ML Engineer role. Highlight your experience in pharma, biotech, or life sciences, and showcase any relevant projects that demonstrate your skills in ML engineering and scientific computing.
Craft a Compelling Cover Letter: Your cover letter should tell us why you're the perfect fit for this role. Share specific examples of how you've transformed ML prototypes into production-ready platforms and your experience with complex scientific datasets.
Showcase Your Technical Skills: Don’t forget to list your technical skills clearly! Mention your proficiency in Python, modern ML tooling, and any hands-on experience with AWS or Azure. We want to see what you can bring to the table!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Nicoll Curtin Technology
✨Know Your Tech Stack
Make sure you’re well-versed in the specific tools and technologies mentioned in the job description, like Python for ML engineering and platforms like AWS and Azure. Brush up on your experience with Databricks, Kubernetes, and any other relevant ML tooling to show you can hit the ground running.
✨Showcase Your Experience
Prepare to discuss your previous work in pharma, biotech, or life sciences. Have concrete examples ready that demonstrate how you've transformed scientific experiments into production-ready ML pipelines. This will help you connect your background directly to the role.
✨Understand MLOps Best Practices
Familiarise yourself with MLOps concepts such as CI/CD, model versioning, and reproducibility. Be ready to explain how you’ve implemented these practices in past projects, as this is crucial for the role and will show your depth of knowledge.
✨Communicate Effectively
Since collaboration with domain scientists is key, practice articulating complex technical concepts in a way that’s easy for non-technical stakeholders to understand. This will demonstrate your strong communication skills and ability to work in a team-oriented environment.
