Research Scientist

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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