Senior Machine Learning Engineer

Arcus Search; Greater London; On-site; GBP 80,000 - 120,000; Full time

Job summary

Arcus Search in London is seeking a Machine Learning Engineer to join a team building the systems and infrastructure behind large‑scale ML. This role focuses on ensuring the technology around ML workloads scales, performs, and remains robust, rather than building models all day.

You’ll work on distributed training, inference, profiling, and optimisation in GPU‑heavy environments, using Python, PyTorch and NumPy.

Qualifications

• Strong engineering judgement and problem-solving skills.
• Experience with distributed training, inference, profiling, and optimisation or HPC.
• Exposure to large-scale distributed systems and performance-sensitive workloads.

Responsibilities

• Improve performance and scalability of ML workloads across training and inference.
• Profile systems, identify bottlenecks, and understand impact of compute, networking, and storage on performance.
• Work with Python, PyTorch, NumPy in GPU-heavy environments and test new frameworks and hardware.

Skills

Python

PyTorch

NumPy

Profiling

Distributed systems

Education

Computer Science or related field

Tools

GPUs

High Performance Computing

Linux

Job description

I'm working with a highly technical organisation in London that is looking for a Machine Learning Engineer to join a team focused on the systems and infrastructure behind large scale ML.

This is less about building models all day and more about making sure the technology around them can handle the scale, performance and complexity required.

What you'll be working on:

• Improving the performance and scalability of ML workloads across training, inference and distributed compute
• Profiling systems, finding bottlenecks and understanding how compute, networking and storage impact performance
• Working with Python, PyTorch, NumPy and other ML tooling in GPU heavy and high performance environments
• Testing new frameworks, hardware and approaches, from quick prototypes through to more robust engineering solutions
• Solving technical problems that need strong engineering judgement rather than an off-the-shelf answer
• Working closely with engineers and researchers across ML, infrastructure and compute

What makes the opportunity interesting:

• You'll be working on ML problems at a scale and level of complexity most companies don't operate at
• The role gives you exposure to large scale distributed systems, serious compute and performance sensitive workloads
• You'll have the freedom to properly investigate new technology rather than just work within an existing stack
• It suits someone who is strong in Python and ML engineering, with experience in areas like distributed training, inference, profiling, optimisation or HPC
• A strong Computer Science, Machine Learning, Maths or related background is useful, although equivalent commercial experience is equally relevant
• You can stay deeply technical and continue solving difficult engineering problems without needing to move into management

This would suit someone who enjoys the engineering side of machine learning and wants to work somewhere scale, performance and technical complexity genuinely matter.