Research Scientist

Research Scientist – AI/ML (Foundation Models & Generative AI)

Artificial Intelligence / Machine Learning / Big Tech / Applied Research

Research & Development environment | Hybrid or on-site | High-collaboration engineering + science team

A research-focused AI/ML role centred on the development, training, and optimisation of large-scale foundation models. The position involves advancing generative AI systems, improving model performance, and translating cutting‑edge research into scalable production‑ready solutions.

• Design and develop large-scale machine learning and foundation models
• Research improvements in architecture, training efficiency, and model performance
• Work on generative AI systems including LLMs and multimodal models
• Build and run large-scale experiments for model training and evaluation
• Develop novel algorithms for representation learning and optimisation
• Analyse model behaviour, performance, and failure modes
• Design datasets and data strategies for model pretraining and fine-tuning
• Work with large-scale distributed training systems
• Improve data quality, filtering, and augmentation methods
• Collaborate with ML engineers to scale research prototypes into production systems
• Optimise models for inference efficiency, latency, and cost
• Use frameworks such as PyTorch, Tensor Flow, or JAX

• Work closely with applied scientists, engineers, and product teams
• Publish research findings in top-tier ML conferences (optional depending on org)
• Contribute to internal research direction and technical strategy

• PhD (preferred) or Master’s in Computer Science, Machine Learning, AI, Mathematics, or related field

• 2–5+ years experience in ML research or applied AI (varies by level)
• Strong background in deep learning and neural networks
• Experience with large-scale model training or distributed systems
• Track record of building or researching transformer-based architectures or similar

• Strong proficiency in Python
• Experience with PyTorch, Tensor Flow, or JAX
• Knowledge of transformers, LLMs, or diffusion models
• Understanding of optimization, GPU training, and scaling ML systems
• Experience with distributed computing or high-performance ML infrastructure

• Strong research and experimental design mindset
• Ability to translate theory into working systems
• Analytical thinking and model debugging skills
• Collaboration across research and engineering teams
• Comfort working in fast-moving, ambiguous R&D environments

• Generative AI innovation
• Research-to-production AI systems