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AI Research Engineer - Frontier Models

Gazelle Global

Research Engineer - Frontier AI & Large Language Models

What if your work could influence the next generation of AI systems?

We're partnering with an innovative AI research organisation that is focused on advancing the capabilities of Large Language Models and intelligent systems.

This is an opportunity to join a team working on challenging problems at the intersection of machine learning research, deep learning, and large-scale AI infrastructure. You'll collaborate with exceptional researchers and engineers to explore new ideas, develop novel approaches, and help shape the future of artificial intelligence.

If you're naturally curious, enjoy tackling difficult technical challenges, and are excited by the prospect of pushing AI capabilities forward, we'd love to speak with you.

The Role:

As a Research Engineer, you'll play a key role in bridging research and implementation. You'll contribute to the development, training, evaluation, and optimisation of advanced AI systems while helping transform research concepts into scalable solutions.

You will work closely with multidisciplinary teams to investigate new techniques, build experimental frameworks, and contribute to the creation of state-of-the-art models and platforms.

Key Responsibilities:

  • Design, develop, and evaluate machine learning models and AI systems
  • Conduct experiments to explore novel approaches and improve model performance
  • Train, fine-tune, and optimise large language models and transformer-based architectures
  • Build scalable infrastructure and tooling to support AI research initiatives
  • Develop robust evaluation frameworks and benchmarking methodologies
  • Collaborate with researchers and engineers to translate ideas into practical implementations
  • Contribute to model efficiency, reliability, safety, and performance improvements
  • Stay at the forefront of developments in machine learning, generative AI, and LLM research
  • Participate in technical discussions, code reviews, and knowledge-sharing initiatives

Required Experience:

  • Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, Physics, or a related discipline
  • Strong understanding of machine learning fundamentals, deep learning, and transformer architectures
  • Hands-on experience with PyTorch, TensorFlow, or similar frameworks
  • Strong programming skills in Python and software engineering best practices
  • Experience developing, training, or evaluating machine learning models
  • Excellent analytical, problem-solving, and experimental thinking abilities
  • Ability to work independently while collaborating effectively within cross-functional teams

Preferred Experience:

  • Experience with Large Language Models and Generative AI systems
  • Knowledge of supervised fine-tuning, model distillation, reinforcement learning, or policy optimisation techniques
  • Experience with distributed training and large-scale machine learning infrastructure
  • Familiarity with search relevance, ranking systems, recommendation systems, or retrieval architectures
  • Publications, research contributions, patents, or meaningful open-source contributions
  • Experience working in research-driven or innovation-focused environments

What Makes This Opportunity Different?

This is not a role focused solely on maintaining existing systems.

You'll be working on problems that require experimentation, creativity, and a genuine passion for advancing AI capabilities. You'll have the opportunity to contribute to projects where the answers are not always known in advance and where innovation is encouraged.

The successful candidate will join a highly collaborative environment that values intellectual curiosity, rigorous thinking, and technical excellence.

If you're interested in contributing to the future of AI and would like to learn more, apply today or contact me directly for a confidential discussion.

Dipika K Grace

View email address on click.appcast.io

Vacancy posted 14 hours ago
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