Research Scientist

We're working with a well-funded, early-stage AI startup tackling one of the most technically challenging problems in modern machine learning.

This is an opportunity to join a small, research-driven team building cutting-edge deep learning systems at the intersection of generative AI, model evaluation, and AI reliability. The team operates with a strong engineering culture where research ideas are rapidly translated into production systems.

You'll have significant ownership over model development, experimentation, and research direction while collaborating closely with experienced researchers and engineers.

What You'll Be Working On

• Designing and training large-scale deep learning models
• Running experiments to improve model accuracy and robustness
• Building and curating large synthetic datasets
• Researching novel techniques across generative AI and machine learning
• Developing production-ready ML systems rather than purely academic research
• Collaborating across research and engineering to bring new capabilities into production
• Publishing technical work where appropriate

We're Looking For Someone Who Has:

• PhD or equivalent industry experience in Machine Learning, Artificial Intelligence, NLP, Computer Vision, or a related field
• Strong research background with publications at leading ML conferences and/or experience building impactful AI products
• Deep understanding of modern generative models and large language models
• Experience training large neural networks using PyTorch
• Strong experimental mindset with the ability to design, execute, and analyse research
• Excellent software engineering skills alongside research experience

Nice to Have

• Experience with synthetic data generation
• Research in adversarial machine learning or model robustness
• Experience with interpretability or explainability techniques
• Background in NLP or computational linguistics
• Experience working with large-scale distributed training or AI infrastructure

Why Join

• Work on technically ambitious problems with real-world impact
• High ownership from day one within a small research team
• Opportunity to influence research direction as the company scales
• Collaborative environment where research and engineering work hand in hand
• Build systems that move quickly from experimentation into production