Research Engineer - Model Ablation

Key responsibilities

• Drive technical work on improving the performance of frontier foundation models — both autoregressive foundation models and world models — for autonomous scientific experimentation.
• Conduct research into the interactions between training data and foundation models to identify the key factors limiting model performance, and develop novel data-centric approaches for continuous improvement.
• Define and source the data needed to move model performance, working closely with domain experts across the laboratory to specify, collect, and curate high-value experimental datasets.
• Design and maintain scalable experimentation pipelines for model training, evaluation, benchmarking, and reproducible research.
• Analyse experimental results using rigorous scientific methodologies and translate insights into actionable improvements for model performance in a data-centric way.
• Collaborate closely with AI researchers, software engineers, robotics engineers, and scientific domain experts to ensure research findings translate into impactful real-world scientific capabilities.
• Contribute novel research ideas and publish high-quality research where appropriate, while maintaining a strong focus on practical deployment.
• Communicate experimental findings and technical insights clearly across interdisciplinary teams to help shape the future direction of the AutoLab AI platform.

Essential knowledge, skills, and experience

• MSc, PhD, or equivalent industry experience in Computer Science, Artificial Intelligence, Machine Learning, Robotics, or a related discipline.
• Hands-on experience with frontier foundation models, including autoregressive foundation models such as Large Language Models (LLMs), Vision-Language Models (VLMs) and Vision-Language-Action (VLA) models, as well as embodied AI models and world models.
• Experience with continuous pre-training, post-training, supervised fine-tuning, reinforcement learning, and other foundation model optimisation techniques.
• Hands-on experience building end-to-end machine learning pipelines, including data curation, data mixing, model training, evaluation, performance analysis, and iterative model improvement.
• Experience working within interdisciplinary teams.
• Excellent written and verbal communication skills, with the ability to communicate complex experimental findings clearly across disciplines.
• An impact-driven mindset with a passion for continuously improving model performance for real world deployment.
• Desirable knowledge, skills, and experience
• In rough order of desirability.
• Research experience in embodied AI and robotics.
• Research or engineering experience with world models, model-based reinforcement learning, or learned simulators.
• Experience in working with scientific domain experts.
• Experience with large-scale distributed training infrastructure.
• Publications at leading AI conferences (e.g. NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, CoRL, RSS, or equivalent).
• Experience working with large multimodal datasets, robotics datasets, or scientific datasets. Experience building scalable and reproducible experimentation and evaluation frameworks.

Personal attributes

We offer the following salary and benefits

• Enhanced holiday pay
• Pension
• Life Assurance
• Income Protection
• Private Medical Insurance
• Hospital Cash Plan
• Therapy Services
• Perk Box
• Electric Car Scheme
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Why work for EIT