Senior ML Engineer, Biomedical AI Systems (Hybrid)

Boehringer Ingelheim GmbH; Aberdeen City, Greater London; Ibrido; GBP 90.000 - 130.000; Tempo pieno

Descrizione del lavoro

Boehringer Ingelheim is seeking a Senior ML Engineer to join the AI Systems team in the UK, bridging research and production for biomedical AI. You will translate research prototypes into robust model artefacts and drive decisions on training, efficiency, and deployment for multimodal data.

You will work closely with AI scientists, maintaining high engineering standards, writing clean, well-tested code, and guiding model handovers to MLOps. This hybrid role includes three days in the office.

Competenze

• Postgraduate degree in Machine Learning, Computer Science or related technical field.
• Solid hands-on experience with deep learning and foundation models at scale.
• Production-quality model artefacts experience and deployment readiness.
• Experience partnering with researchers during implementation.
• Python proficiency and deep learning frameworks (PyTorch or JAX).
• Strong software engineering fundamentals with version control and docs.
• Experience with distributed training frameworks like PyTorch Distributed or DeepSpeed.

Mansioni

• Bring ML engineering into architectural design for scalable foundation models.
• Implement training code, data loaders, tokenisers, inference logic and fine-tuning interfaces.
• Collaborate with AI scientists to productionise validated prototypes.
• Optimize models and inference pipelines for latency and efficiency.
• Lead handovers to MLOps with thorough documentation.

Conoscenze

Python

Deep Learning

Transformer models

Distributed training

Code reviews

Formazione

Postgraduate degree in ML/CS/Computational Biology

PhD preferred or MSc with equivalent experience

Strumenti

PyTorch

JAX

DeepSpeed

Ray Train

Descrizione del lavoro

Boehringer Ingelheim is seeking a Senior ML Engineer to join the AI Systems team in the UK, bridging research and production for biomedical AI. You will translate research prototypes into robust model artefacts and drive decisions on training, efficiency, and deployment for multimodal data.

You will work closely with AI scientists, maintaining high engineering standards, writing clean, well-tested code, and guiding model handovers to MLOps. This hybrid role includes three days in the office.