Research Engineer in AI

Responsibilities:

• Algorithmic Optimisation: Research and understand the latest deep learning literature to implement and optimise state-of-the-art algorithms and architectures, ensuring compute efficiency and performance.
• Algorithmic Design: Identify limitations in the application of existing state-of-the-art algorithms for our desired use cases, and propose concrete algorithmic contributions to mitigate those limitations and improve upon existing state-of-the-art.
• Scaling Expertise: Design and implement strategies to efficiently scale machine learning models across different accelerator platforms (GPU/TPU).
• Performance Optimisation: Analyse and profile ML systems under heavy load, pinpointing bottlenecks and implementing targeted optimisations.
• Distributed Systems Architecture: Create robust distributed training and inference solutions for maximum computational efficiency.

Required Skills:

• A Masters or equivalent in Computer Science, Engineering, Mathematics, or related field.
• Fundamentals of modern Deep Learning and experience with Reinforcement Learning.
• 2+ years of work experience.
• Expertise in Python.
• Experience working with Linux systems.
• Experience with at least one modern machine learning framework (JAX, Tensorflow, PyTorch, etc.)
• Experience with Docker.

Highly Desirable:

• Experience working with JAX and packages within the JAX ecosystem.
• Track record of successfully building and scaling ML models.
• Experience with software profiling, identifying bottlenecks, and delivering efficient solutions.