AI Research Engineer

AI Research Engineer – Agents & Intelligent Systems

Exciting opportunity to join a well-funded early-stage AI company building advanced AI systems that tackle complex technical workflows. The team combines cutting-edge research with product development, creating intelligent systems that can reason, plan, and execute sophisticated tasks in real-world environments.

About the Role

• You'll research, design, and deploy novel AI systems that combine large language models, tool use, planning, and autonomous decision-making to solve challenging technical problems.
• You'll evaluate and improve system performance through rigorous experimentation, benchmarking, and data-driven analysis across a range of real-world use cases.
• You'll stay at the forefront of AI research, translating the most impactful advances from academia and open-source communities into practical product capabilities.
• You'll investigate effective approaches to reasoning, orchestration, prompt design, model adaptation, and system optimisation using a scientific and experimental mindset.
• You'll write high-quality production code and build tooling that bridges research and real-world deployment.
• You'll collaborate closely with a small, experienced team working on ambitious technical challenges at the intersection of AI research and software systems.

What We're Looking For

• Master's degree, PhD, or equivalent industry experience in Computer Science, Mathematics, Physics, Machine Learning, or a related field.
• Strong track record in AI/ML research or engineering, ideally involving language models, autonomous systems, or complex decision-making workflows.
• Excellent understanding of machine learning fundamentals and experimental methodology.
• Strong Python skills and experience with modern ML frameworks such as PyTorch, TensorFlow, or JAX.
• Experience working with LLM APIs, evaluation frameworks, cloud infrastructure, containers, and production software systems.

Nice to Have

• Experience adapting, fine-tuning, or evaluating large language models.
• Familiarity with compiler design, program synthesis, or code generation principles (MLIR, TVM, LLVM).
• Experience with GPU accelerated kernels (e.g. CUDA, OpenCL, ROCm) for Nvidia and AMD GPUs.
• Contributions to open-source projects or published research in relevant areas.
• Experience working on performance-critical software, distributed systems, or large-scale infrastructure.