Senior Machine Learning Engineer in Cambridge

Applicants for this role should have strong experience working with machine learning systems and frameworks, along with a solid understanding of core AI concepts and model behaviour. The ideal candidate is an experienced ML engineer who understands how contemporary models are trained and executed, and who has hands‑on experience debugging functional and performance issues in ML systems. This person will be comfortable working with industry‑standard frameworks and state‑of‑the‑art models, bringing them up on internal infrastructure, and collaborating closely with software and hardware teams in a technically demanding environment spanning ML frameworks, infrastructure, and AI accelerator hardware.

• Experience working in Machine Learning or ML‑adjacent engineering roles
• Strong foundation in core AI and ML concepts (e.g. neural networks, training vs inference, numerical precision, performance trade‑offs)
• Hands‑on experience with one or more major ML frameworks such as PyTorch, TensorFlow, JAX, or similar
• Strong proficiency in Python for ML workflows, experimentation, and automation
• Experience designing, running, and analysing ML benchmarks or experiments
• Experience working in Linux environments
• Strong analytical and debugging skills, with the ability to reason about model behaviour and system performance
• Bachelor/Master's/PhD or equivalent experience in Computer Science, Maths, Machine Learning, Data Science, or related field (Desirable)
• Experience with MLOps pipelines, model deployment, or production ML systems (Desirable)
• Familiarity with performance analysis, profiling tools, or numerical accuracy validation (Desirable)
• Exposure to distributed training or inference systems (Desirable)
• Experience with hardware‑accelerated ML, compilers, or system‑level performance considerations (Desirable)
• Familiarity with CI/CD systems used for ML workflows (Desirable)
• Experience contributing to open‑source ML frameworks or tooling

The role centres on testing, validating, and benchmarking a complex ML software stack, with a particular focus on performance, reliability, and correctness across modern AI workloads. The ML QA team is composed of highly skilled software engineers with a strong focus on automation, software quality, and data‑driven validation. The team works closely with industry‑standard machine learning frameworks and models, contributing to upstream open‑source projects and collaborating across the wider software organization.

Operating in a fast‑paced environment, the team plays a critical role in ensuring reliability, performance, and maintainability across the ML software stack, helping to deliver robust and high‑quality products to customers.

• Benchmark ML models and frameworks, analysing results to identify regressions, performance bottlenecks, and correctness issues
• Work hands‑on with industry‑standard ML frameworks to validate functionality and performance across different execution environments
• Build and maintain automated testing and benchmarking pipelines targeting simulators, emulators, and physical hardware
• Collaborate closely with software teams to ensure adequate test coverage for new and existing features
• Develop tooling and scripts (primarily in Python) to support testing, benchmarking, and functional reporting
• Take ownership over aspects of our testing and infrastructure, owning the roadmap and driving innovation independently