ML Engineer

Boehringer Ingelheim GmbH; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Boehringer Ingelheim GmbH in London is seeking an ML Engineer to join the AI Systems team. You will help translate validated research into production-ready models and contribute to scalable training and inference pipelines.

You will collaborate with AI scientists and other ML engineers to ensure production-quality artefacts, with an emphasis on efficiency, scalability, and rigorous engineering standards. This hybrid role requires 3 days in the office.

Qualifications

• Postgraduate degree in Machine Learning, Computer Science, Computational Biology or related field; PhD preferred or MSc with equivalent industry experience.
• Hands-on experience with deep learning and foundation model implementations such as transformers, pre-training and fine-tuning.
• Some experience contributing to production-quality model artefacts, moving from research to deployment.
• Experience collaborating with researchers during implementation.
• Proficiency in Python and PyTorch.
• Strong software engineering fundamentals: clean, tested, well-documented code, version control, reviews.
• Knowledge of distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP or Ray Train.
• Exposure to model optimisation techniques for inference (quantisation, distillation, pruning).

Responsibilities

• Contribute engineering perspectives to architectural design discussions to ensure scalable foundation models for training and inference.
• Under supervision, implement biomedical foundation model components like training code, data loaders, tokenisers, and inference logic to a high standard.
• Collaborate with AI scientists to translate validated research prototypes into production-ready model artifacts and benchmarks.
• Optimise validated models and inference pipelines to meet production latency and efficiency requirements.
• Write clean, well-tested, well-documented code and adhere to team engineering standards.
• Contribute to model handovers to MLOps engineers with documentation on capabilities, limitations, and retraining criteria.
• Stay updated on advances in ML engineering and biomedical AI tooling.

Skills

Python

PyTorch

Deep learning

Distributed training

Software engineering

MLOps

Model optimisation

Education

Postgraduate degree in ML/CS/Computational Biology

Tools

PyTorch Distributed

DeepSpeed

FSDP

Ray Train

Job description

Most diseases are still poorly understood at a biological level. Despite decades of research, the causal mechanisms driving many conditions remain unclear, limiting our ability to identify the right targets, design the right interventions and bring the right medicines to patients.

The AI Accelerator exists to change that. Based in London and sitting within Computational Innovation (@computationalinnovation), a global organisation spanning computational biology, human genetics, data excellence and AI, the Accelerator’s mission is to build production-quality AI capabilities that deepen our understanding of disease biology and increase probability of success.

We do this by applying neural-based methods across the biomedical data landscape to integrate heterogeneous, multimodal data sources, infer biological relationships and embed causal thinking into what we build. The goal is not just to predict but to explain and understand why disease occurs.

It could be electronic health records and medical imaging to support patient segmentation. It could be ‘omics data to identify novel therapeutic targets. It could be predicting transcriptional change for a given disease-causing variant. It could be simulating the effect of modulating a target of interest.

A core component of the AI Accelerator is AI Systems, a team focused on designing, building and deploying multimodal foundation models across the vast biomedical data landscape that will be used within Computational Innovation to enhance and accelerate portfolio decision-making.

THE POSITION

We are looking for an ML Engineer to join the AI Systems team and contribute to work at the frontier of biomedical AI. This is a hands-on engineering role with real stakes, as the models you help build will be used to make decisions about which indications to pursue, in which patient population and against which target.

You will work in close partnership with AI scientists and more experienced ML engineers, supporting the translation of validated research prototypes and architectural designs into production-ready implementations at a high engineering standard. You will contribute to early architectural discussions, offering engineering perspectives on training, efficiency, scalability and production-readiness, and iterating with the team on design decisions under senior guidance.

This is a role for someone who takes pride in engineering craft, who writes clean, well-tested, well-documented code, and who cares that biology retains its integrity as models move from research to production. Your engineering work will go far beyond the model card; it will connect directly to human health outcomes.

Key Responsibilities

• Contribute engineering perspectives to architectural design discussions, supporting decisions that ensure foundation models train and infer efficiently and are scalable
• Under the guidance of senior ML engineers, implement biomedical foundation model components such as training code, data loaders, tokenisers, inference logic and fine-tuning interfaces to a high engineering standard
• Work closely with AI scientists to help translate validated research prototypes into robust, production-quality model artefacts, and contribute to benchmarking and performance evaluation
• Contribute to the optimisation of validated models and inference pipelines, applying techniques such as quantisation, distillation and pruning to help meet production efficiency and latency requirements without compromising model performance
• Write clean, well-tested, well-documented code and follow the engineering standards set by the team
• Support model handovers to MLOps engineers, contributing documentation covering capabilities, known limitations, failure modes and retraining criteria
• Stay current with advances in ML engineering, distributed training and biomedical AI tooling

Required Qualifications

• Postgraduate degree in Machine Learning, Computer Science, Computational Biology or a related technical field; PhD preferred or MSc with equivalent industry experience
• Hands-on experience with deep learning and foundation model implementations such as transformers, pre-training and fine-tuning
• Some experience contributing to production-quality model artefacts, with a growing understanding of what is required to move from research prototypes to reliable deployment
• Experience collaborating with researchers during the implementation process
• Proficiency in Python and deep learning frameworks such as PyTorch
• Strong software engineering fundamentals such as writing clean, testable, well-documented and maintainable code, version control, code reviews
• Working knowledge of distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP or Ray Train
• Exposure to model optimisation techniques for inference, e.g. quantisation, distillation, pruning

Preferred Qualifications

• Experience working with biomedical data modalities such as genomics, multi-omics, clinical or imaging data in an ML context is advantageous
• Publications or contributions to open-source ML projects or tooling

This is a hybrid role with approximately 3 days a week in the office

WHY THIS IS A GREAT PLACE TO WORK

Boehringer Ingelheim has been recognised as aTop Employer in the UK, demonstrating our commitment to building an exceptional workplace through strong people practices and supportive HR policies.

To learn more about why BI is a great place to work, visit: