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