BoehringerPRD
Senior ML Engineer
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The AI Accelerator
Most diseases remain poorly understood at a biological level. Despite decades of research, causal mechanisms driving many conditions remain unclear, hindering our ability to identify targets, design interventions, and develop medicines effectively.
The AI Accelerator aims to change that. Based in London and part of Computational Innovation (@computationalinnovation), a global organisation specialising in computational biology, human genetics, data excellence, and AI, the Accelerator designs production-quality AI capabilities that deepen our understanding of disease biology and improve the probability of successful medical advancements.
We apply neural-based methods across the biomedical data landscape, integrating heterogeneous and multimodal sources, inferring biological relationships, and embedding causal thinking into our models. Our goal goes beyond prediction—our mission is to explain and understand why disease occurs.
This could involve:
- Using electronic health records (EHRs) and medical imaging for patient segmentation.
- Leveraging ‘omics data to uncover novel therapeutic targets.
- Simulating the effects of modulating targets of interest.
- Predicting transcriptional changes caused by disease-causing variants.
A core part of the AI Accelerator is AI Systems, a team dedicated to designing, building, and deploying multimodal foundation models that will enhance and accelerate decision-making across Computational Innovation’s portfolio.
The Position: Senior ML Engineer
We are recruiting a Senior Machine Learning Engineer to join the AI Systems team and work at the forefront of biomedical AI. This hands-on role carries real-world impact, as the models you develop will directly influence decisions about which diseases to target, for which patient populations, and against which therapeutic targets.
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You will collaborate closely with AI scientists, transitioning validated research prototypes and architectural designs into production-grade infrastructure. Your role will involve contributing engineering insights on training, efficiency, scalability, and deployment-readiness, while iterating with AI scientists to refine designs.
This is an opportunity for an engineer who values craftsmanship:
- Someone who writes clean, thoroughly tested, and well-documented code.
- Ensures that biological relevance is preserved when moving research from labs to production.
- Accepts that your work doesn’t just produce a model—it directly impacts patient outcomes.
Key Responsibilities
- Contribute engineering excellence early in architectural decisions to ensure foundation models are efficient, scalable, and deployment-ready.
- Implement core model components, including:
- Training code
- Data loaders
- Tokenisers
- Inference logic
- Fine-tuning interfaces
- Maintain high technical standards throughout development.
- Partner with AI scientists, translating validated prototypes into production-ready models, and contribute to benchmarking and evaluation efforts.
- Develop and maintain clean, tested, and well-documented code, upholding rigorous software engineering practices across the team.
- Lead model handovers to MLOps engineers, providing clear documentation on:
- Model capabilities
- Known limitations
- Failure modes
- Retraining criteria
- Stay updated on advancements in ML engineering, distributed training, and biomedical AI tools, sharing insights with the team.
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Required Qualifications
- PhD in Machine Learning, Computer Science, Computational Biology, or a related quantitative field.
- Extensive hands-on experience with deep learning and foundation model implementations (e.g., transformers, pre-training, fine-tuning, preferably at scale):
- Proven track record of delivering production-quality model artefacts, with a deep understanding of the challenges in transitioning from research prototypes to deployment.
- Proficiency in Python and deep learning frameworks such as PyTorch or JAX.
- Strong software engineering fundamentals, including:
- Writing clean, testable, well-documented code.
- Version control and code reviews.
- Experience with distributed training frameworks, including PyTorch Distributed, DeepSpeed, FSDP, or Ray Train.
- Prior biomedical data expertise (e.g., genomics, multi-omics, clinical, or imaging data) in a machine learning context, considered advantageous:
- Experience collaborating closely with researchers from prototyping to implementation—critical for translating scientific breakthroughs.
- Record of publications or contributions to open-source ML projects/tooling.
Additional Notes
- Interviews for shortlisted candidates will take place between 20th and 31st July.
- This is a hybrid role, with approximately 3 days per week in the office.
Why Join Us?
Boehringer Ingelheim has been recognised as a Top Employer in the UK, reflecting our commitment to fostering an exceptional workplace.
For more details on workplace culture and benefits, explore: 🔗 Why Work at Boehringer Ingelheim (UK)
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