Staff AI Scientist

Boehringer Ingelheim GmbH; Greater London; On-site; GBP 90,000 - 150,000; Full time

Job summary

Boehringer Ingelheim London in Computational Innovation's AI Accelerator seeks a Staff AI Scientist to design and develop state-of-the-art biomedical foundation models. You will learn from multi-omics, clinical and imaging data and enable downstream teams to deploy AI capabilities for patient segmentation and target discovery.

You will lead significant research programs, mentor junior scientists and publish findings in peer-reviewed venues, while collaborating with ML Engineers to productionize

Qualifications

• PhD in machine learning, computer science, computational biology, bioinformatics or a related quantitative field.
• Strong publication record in ML or biomedical AI venues.
• Deep understanding of foundation models such as transformers and self-supervised learning.
• Proficiency in Python and deep learning frameworks (PyTorch or JAX).
• Experience leading in a matrixed organization as a technical lead.

Responsibilities

• Lead multimodal biomedical foundation model design across at least two modalities from multi-omics, clinical and imaging data.
• Develop and validate reproducible research with well-documented code for ML Engineers to implement.
• Design rigorous experiments to test multi-modal architectures and validate results.
• Collaborate with Applied AI teams, computational biologists and human geneticists to align models with downstream therapeutics.
• Work with ML Engineers to ensure production-ready components and architectural integrity.
• Publish findings in peer-reviewed ML and biomedical AI venues.
• Mentor Senior AI Scientists to develop greater research independence.

Skills

Python

PyTorch

JAX

Multimodal ML

Education

PhD in ML/CS/Computational Biology/Bioinformatics

Tools

PyTorch

JAX

Docker

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 seeking a Staff AI Scientist to join AI Systems within Computational Innovation’s AI Accelerator. In this role, you will design and develop state of the art biomedical foundation models that learn rich representations from multi-omics, clinical and imaging data, advancing the field while directly enabling downstream teams to deploy AI capabilities to computational biologists and human geneticists that segment patient populations, uncover novel, disease-driving biology and ground that biology in genetics.

You will be a highly experienced, independent AI Scientist within AI Systems, owning complex research workstreams and contributing meaningfully to the scientific direction of the team. Working within the wider research strategy and architectural vision set by the Senior Staff AI Scientist, you will own significant research programmes and bring deep specialist expertise across AI-driven life sciences, growing your influence over the team’s broader research agenda over time.

This is a hands-on role with responsibility for leading a multidisciplinary squad that executes on AI Accelerator initiatives. You will work in close partnership with ML Engineers throughout the research and implementation process and contribute to the development of more junior AI Scientists.

KEY RESPONSIBILITIES

Own and lead significant research workstreams in multimodal biomedical foundation model design, bringing deep specialist expertise across at least two biomedical data modalities from multi-omics, clinical and health records, medical imaging.

Develop and validate novel approaches for learning from biomedical data, producing well-documented and reproducible research that ML Engineers can implement effectively.

Design and run rigorous experiments to test multi-modal model architectures, training approaches and methods, interpreting results with scientific rigour and validating outputs against biomedical expectations as well as statistical benchmarks.

Work closely with the Applied AI teams and computational biologists and human geneticists to understand needs and ensure models are developed with downstream therapeutic applications in mind.

Work closely with ML Engineers throughout the research and implementation process, ensuring architectural decisions are scientifically ambitious, practically feasible and accurately reflected in production model components.

Publish research findings in peer-reviewed ML and biomedical AI venues, contributing to the team’s scientific credibility and external profile.

Mentor Senior AI Scientists by providing technical guidance, reviewing experimental designs and helping them develop greater research independence.

REQUIREMENTS

PhD in machine learning, computer science, computational biology, bioinformatics or a related quantitative field, with a strong publication record in ML or biomedical AI venues relative to career stage.

Deep understanding of typical foundation model architectures such as transformers and expertise in self-supervised learning, pre-training, transfer learning and fine-tuning.

Strong software engineering practices, including clean, testable, well-documented and reproducible research code, with proficiency in Python and deep learning frameworks such as PyTorch or JAX.

Familiarity with distributed training frameworks and large-scale biomedical data processing, with demonstrated ability to take research from novel idea through rigorous experimentation to validated prototype.

Strong ability to engage with computational biology and human genetics questions in the context of drug discovery and translate them into research directions that deliver meaningful capability to Applied AI and Analytics.

Experience operating in a matrixed setting as a technical lead.