Job Description
Research Scientist – AI/ML (Foundation Models & Generative AI)
Industry
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
Foundation Model Research
- 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
Model Development & Experimentation
- 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
Data & Training Pipelines
- 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
Engineering & Implementation
- 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
- Foundation model development
- Generative AI innovation
- Large-scale ML experimentation
- Research-to-production AI systems
- Advanced deep learning architecture design
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