Machine Learning Research Engineer

Requirements:

• PhD in a technical subject with a major engineering component and exposure to AI/ML, or BSc, MSc and 2+ years of specific experience working on ML model development.
• Strong general engineering experience, as evidenced by exposure to one or more of:
• Software design / algorithms, especially for deep learning frameworks.
• Modern ML frameworks such as JAX, PyTorch or TensorFlow.
• Distributed systems and runtimes.
• Compilers (e.g. XLA, Triton, CUDA, Pallas).
• Large scale model training and serving infrastructure.
• Experience in navigating complex research codebases.
• Databases and data processing pipelines.
• Numerical methods, simulation, optimisation.
• Strong fundamentals in mathematics, statistics, linear algebra.
• Experience with the full ML research and development lifecycle.
• Strong understanding of ML theory and applications.
• Strong understanding of data structures and algorithms.
• (Desirable) Interest in chemistry and biology.
• (Desirable) Experience working with biomedical data.
• (Desirable) Knowledge of the pharmaceutical industry, ideally with a focus on drug discovery.

What the job involves:

• We are looking for Research Engineers with different levels of experience - Mid through to Senior, Staff, Principal or equivalent levels.
• This is an exciting opportunity for you to contribute to frontier research at the intersection of AI and drug design.
• Working in a highly creative, iterative environment, you will be partnering with scientists and engineers to advance foundational models that will transform the biopharmaceutical world.
• You will draw upon your existing engineering and Machine Learning experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology and chemistry problems.
• Translate research concepts into practical implementations by developing and optimising state-of-the-art AI models, and building and maintaining robust codebases, data pipelines, and infrastructure for training and evaluation.
• Design, implement, and run experiments to evaluate the performance and robustness of ML models, using a full spectrum of state-of-the-art machine learning methods.
• Evaluating, tuning, and maintaining AI/ML models (which includes collecting and preparing data as needed).
• Implement algorithms and software to analyse and evaluate the performance of AI models.
• Optimising performance of AI/ML models leveraging a deep understanding of the AI/ML hardware and software stack.
• Advise on how to bring AI/ML models to production and/or integrating them into product offerings, and monitoring and refining their behaviour.
• Developing specialised tools/frameworks/infrastructure to aid in the work above.
• Work closely with research scientists and engineers, contributing to team discussions, sharing knowledge, and actively participating in code reviews to foster a collaborative environment.
• Proactively identify and address technical challenges, stay updated on the latest AI advancements, and focus on developing solutions that enable scaling our wider foundation and applied model platforms.
• Ability to execute on independent engineering projects and software development towards research goals.