ML Research Engineer (AI)

Location: London, hybrid, with attendance at the office 3 days a week (currently Tuesday, Wednesday, and one other day depending on the team).

Company

hirify.global builds AI models and a drug design engine to accelerate scientific discovery and develop innovative medicines.

What you will do

• Translate research concepts into practical implementations by developing and optimising state-of-the-art AI models.
• Build and maintain codebases, data pipelines, training infrastructure, and evaluation systems.
• Design and run experiments to evaluate, tune, benchmark, and improve the robustness of machine learning models.
• Develop algorithms and specialised tools for model analysis, inference, production integration, monitoring, and refinement.
• Collaborate with research scientists and engineers, participate in code reviews, and share technical knowledge.
• Independently lead engineering projects that address technical challenges and scale foundation and applied model platforms.

Requirements

• Master’s degree, PhD, or equivalent practical experience in a quantitative field such as computer science, AI, physics, or mathematics.
• Deep understanding of machine learning principles and strong proficiency in JAX or PyTorch.
• Hands-on experience with modern architectures such as Transformers, GNNs, and Diffusion Models.
• Experience across the full machine learning lifecycle, including data analysis, training, debugging, evaluation, benchmarking, and deployment.
• Excellent software development skills, including strong algorithms and data structures fundamentals.
• Strong written and verbal communication skills, self-direction, and the ability to work across scientific and engineering disciplines.

Nice to have

• Scientific publications or significant contributions to state-of-the-art AI models.
• Experience with distributed multi-GPU or multi-node training and performance optimisation using tools such as XLA, Triton, CUDA, or Pallas.
• Interest in or knowledge of biochemistry, computational biology, or drug discovery.
• Experience developing applied machine learning models for real-world use cases.
• Infrastructure and low-level engineering experience with GCP, Kubernetes, or Docker.

Culture & Benefits

• Work in an interdisciplinary environment combining machine learning, engineering, biology, chemistry, and drug discovery.
• Collaborate closely with scientists and engineers in a creative and iterative research setting.
• Shared values emphasise thoughtful, brave, determined, and collaborative work.
• Hybrid working supports knowledge sharing and in-person relationships.
• Inclusive employment practices and workplace accommodations are available for additional needs.