Machine Learning Performance Engineer

Machine Learning Performance Engineer Overview

We are seeking a talented Machine Learning Performance Engineer to join our engineering team at G-Research. In this role, you will play a crucial part in optimizing large-scale workloads across our GPU and CPU infrastructure. This hands-on position is designed for individuals who are eager to make a significant impact by enhancing the performance and capabilities of our research workloads on cutting-edge computing systems.

Your responsibilities will include collaborating with internal research teams and infrastructure engineers to profile and analyze workloads, eliminate bottlenecks, and develop reference solutions. Your contributions will directly influence the long-term evolution of our platform, shaping the architecture, software stack, and tools that support large-scale machine learning computations.

Key Responsibilities

• Collaborate with researchers, senior stakeholders, and engineers to identify compute challenges and design optimized solutions.
• Profile, benchmark, and tune large-scale training and inference workloads for performance on distributed CPU and GPU systems.
• Develop reference implementations, libraries, and tools to enhance job efficiency and reliability.
• Work closely with systems, architecture, and platform teams to advance the compute stack.
• Influence long-term decisions regarding platform and infrastructure development.

Who Are We Looking For?

The ideal candidate will possess the following qualifications:

• Bachelor’s, Master’s, or PhD degree in computer science or a related field, or equivalent professional experience.
• Demonstrated experience in profiling, benchmarking, and optimizing distributed workloads.
• Proficiency in Python programming.
• Familiarity with CUDA for GPU programming.
• Experience with high-performance computing (HPC) schedulers and Kubernetes for workload orchestration.
• Strong knowledge of deep learning frameworks, particularly PyTorch.
• Solid understanding of data structures, algorithms, and parallel programming on heterogeneous systems.
• In-depth knowledge of Linux operating system fundamentals, including scheduling, memory management, NUMA, networking, and filesystems.
• Experience with profiling and monitoring tools such as nsys, ncu, eBPF-based tools, and performance counters.
• Excellent communication skills and the ability to collaborate effectively across research, infrastructure, and engineering teams.

Why Should You Apply?

We offer a highly competitive compensation package along with an annual discretionary bonus. Additional benefits include:

• Lunch provided through Just Eat for Business and access to a dedicated barista bar.
• 35 days of annual leave.
• 9% contributions to the company pension scheme.
• Informal dress code and a strong emphasis on work/life balance.
• Comprehensive healthcare and life assurance coverage.
• Cycle-to-work scheme available.
• Monthly company events to foster team bonding.
• Relocation support for those moving to London, including coverage of relocation and immigration costs, personal consultant services, and immigration guidance.

If you are ready to take the next step in your career and make a significant impact in the field of machine learning, we encourage you to apply for this exciting opportunity.

Degree Requirement: Bachelor’s

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