Job summary
Autonomous Labs is building the next generation of AI-driven scientific laboratories, with the mission of automating the entire scientific discovery workflow - from experimental design and execution to analysis and iterative learning. Foundation models are a core component of this vision, enabling intelligent, adaptive, and autonomous scientific experimentation.
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
- Curious and analytical, with a passion for understanding why foundation models succeed or fail.
- Strong scientific thinking combined with practical engineering skills.
- Comfortable working in an iterative, fast-paced research environment.
- Collaborative and proactive, with the ability to work effectively across AI, robotics, software engineering, and scientific disciplines.
- Organised, resourceful, and capable of managing multiple experimental projects simultaneously.
- Mission-driven, with a desire to accelerate scientific discovery through frontier AI research.
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
Why work for EIT:
At the Ellison Institute, we believe a collaborative, inclusive team is key to our success. We are building a supportive environment where creative risks are encouraged, and everyone feels heard. Valuing emotional intelligence, empathy, respect, and resilience, we encourage people to be curious and to have a shared commitment to excellence. Join us and make an impact!
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