Senior Machine Learning Engineer (Large Systems)
Salary not listed • Bristol, UK • Full-time
Posted 22d ago
Job Location
Bristol, UK
Also open in Gdańsk, Pomeranian Voivodeship, Poland, Cambridge, UK, London, UK
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
As a Senior Machine Learning Engineer in the Applied AI team, you will contribute to advancing AI technology by developing and optimizing new and existing AI models for specialized hardware. You will work on large-scale systems where performance is critical to project success, with visibility across the entire pipeline from hardware to AI applications. This role involves close collaboration with researchers in a rapidly evolving field, requiring continuous learning and adaptation to new challenges. We seek engineers with strong technical foundations who are curious and eager to understand and advance AI model implementation at scale. This position offers flexibility through a hybrid working model from our UK offices.
Responsibilities
- Implement and train state-of-the-art machine learning models, optimizing for performance, accuracy, and scalability across systems with thousands of accelerators.
- Benchmark and profile ML models to identify performance bottlenecks.
- Develop a deep understanding of the software stack to optimize kernel implementations.
- Test and evaluate new internal software releases, provide feedback to software engineering teams, fix code, and conduct code reviews.
- Design and conduct experiments on novel AI methods and evaluate results.
- Collaborate with Research, Software, and Product teams to define, build, and test next-generation AI hardware.
- Engage with the AI community and stay updated on the latest developments.
Requirements
- Bachelor's/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field.
- Proficiency in deep learning frameworks like PyTorch/JAX.
- Strong Python or C++ software development skills.
- Expertise in hardware-accelerated deep learning, from model training to optimization and evaluation.
- Ability to design, execute, and report on ML experiments.
- Well-developed understanding of performance bottlenecks and how to overcome them.
- Ability to move quickly in a fast-moving field.
- Enjoy cross-functional work and collaboration with other teams.
- Strong communication skills, able to explain complex technical concepts to different audiences.
- Experience in MLOps for Kubernetes-based clusters (desirable).
- Experience building production systems with large language models (desirable).
- Experience in efficient computing based on low-precision arithmetic (desirable).
- Experience writing C++/Triton/CUDA kernels for performance optimization of ML models (desirable).
- Experience in distributed training or inference of ML models across 64+ accelerators (desirable).
- Familiarity with HPC systems and networking (e.g., Infiniband, NVLink, RoCE) and cloud computing platforms (desirable).
- Contribution to open-source projects or published research papers (desirable).
- Keenness to present, publish, and deliver talks in the AI community (desirable).
Benefits
- Flexible working
- Generous leave
- Retirement planning support (up to 5% matched pension)
- Phantom equity
- Workplace experience (free food and drinks)
- Income protection and life assurance
- Electric Vehicle Scheme
- Flexible benefits (private medical insurance, dental cover)
- Optional benefits (dental cover, health cash plan, private medical insurance, cycle to work scheme, give as you earn)