Research Engineer Machine Learning

Aspire Life Sciences Search

Work closely with research and engineering teams to integrate generative AI and machine learning models into the company s discovery platform.

Translate research prototypes into well-structured, maintainable code suitable for production-level workflows.

Design and maintain infrastructure to support data ingestion, preprocessing, training, inference, and evaluation at scale.

Optimise distributed training and inference pipelines, including the use of GPUs and cloud or cluster computing environments.

Implement monitoring, logging, experiment tracking, and reproducibility best practices across ML workflows.

Partner with scientists and domain experts to accelerate experimentation cycles and improve research productivity.

Contribute to engineering standards through documentation, code reviews, and shared best practices.

Required experience and skills

• MSc or PhD in Computer Science, Mathematics, Statistics, or a related technical field (or equivalent research/industry experience).
• Experience working in fast-paced research or engineering environments, ideally within smaller or early-stage teams.
• Demonstrated ability to build and maintain machine learning infrastructure for large-scale training, inference, and deployment.
• Experience working with complex research codebases and contributing to or extending open-source frameworks.
• Strong proficiency with PyTorch and wider ML engineering tooling, including Docker, Kubernetes, CI/CD systems, and cloud platforms.
• Solid software engineering fundamentals, including testing, reproducibility, version control, and documentation.
• Excellent communication skills and a proactive, delivery-focused working style.

Nice to have

• Experience with experiment-tracking and model-monitoring frameworks.
• Familiarity with computational chemistry, bioinformatics, or molecular simulation tools (e.g., RDKit, OpenMM).
• Background with infrastructure-as-code, cloud orchestration, or GPU cluster management.