Research Engineer

This role is within a fast-growing technology company developing advanced machine learning systems for real-world, data-intensive applications. The organisation focuses on adapting general-purpose foundation models to highly specific contexts, delivering high-value, production-ready solutions at scale.

As an Applied Research Engineer, you will own the end-to-end lifecycle of post-training ML models, from adaptation and validation to deployment. You will make core technical decisions that determine model performance and usability in real-world settings, collaborating closely with ML infrastructure and domain teams.

• Ownership of the full post-training workflow for foundation models in production environments
• Responsibility for translating research into practical, high-impact ML applications
• Close collaboration with engineering, product, and domain experts
• Exposure to large-scale model adaptation, distributed training, and experimental design
• A high-autonomy role in a fast-paced, engineering-led environment
• Competitive compensation and long-term growth opportunities

• Design and implement post-training pipelines to adapt foundation models to specific use cases
• Build validation frameworks that link model improvements to real-world or domain-specific metrics
• Lead experiments end-to-end: from hypothesis through distributed training runs to analysis and deployment
• Collaborate with ML engineers and domain experts to ensure outputs are scientifically or technically meaningful
• Contribute to internal and open-source tooling, helping shape the technical direction of post-training capabilities
• Keep up-to-date with post-training research and integrate relevant advances into production

• MSc or PhD in Machine Learning, Computational Biology, or a related technical field, or equivalent experience
• Hands-on experience with post-training techniques such as fine-tuning, LoRA, DPO, RLHF, or similar alignment methods
• Strong Python and PyTorch skills; comfortable with training loops, distributed runs, and model internals
• Familiarity with modern ML architectures, particularly Transformers
• Experience designing and executing experiments rigorously, tracking metrics, and drawing valid conclusions
• Ability to work autonomously and make decisions with incomplete information
• Strong communication skills to explain technical trade-offs across teams

• Experience with foundation models in a scientific or technical domain
• Familiarity with distributed multi-GPU or multi-node training frameworks
• Contributions to open-source ML tooling or relevant publications
• Experience integrating post-training improvements into production systems