Research Scientist

Research Scientist – AI/ML (Foundation Models & Generative AI) Artificial Intelligence / Machine Learning / Big Tech / Applied Research Work Setting Research & Development environment | Hybrid or on-site | High-collaboration engineering + science team Role Overview 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. Key Responsibilities 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, TensorFlow, or JAX Collaboration & Research Output 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 Requirements Education PhD (preferred) or Master’s in Computer Science, Machine Learning, AI, Mathematics, or related field Experience 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 Technical Skills Strong proficiency in Python Experience with PyTorch, TensorFlow, 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 Core Competencies 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 Role Focus Generative AI innovation Research-to-production AI systems #J-18808-Ljbffr