Machine Learning Engineer (Autonomous Systems)

Understanding Recruitment; Greater London; Hybrid; GBP 90,000 - 115,000; Full time

Job summary

Understanding Recruitment is seeking a Machine Learning Engineer to help build autonomous systems in London with hybrid work arrangement (3 days per week on site).

You’ll contribute across ML engineering, MLOps and data engineering, training and optimizing deep learning models, and deploying them to real-world autonomous platforms using Python, C++ or Rust, and leveraging Docker/Kubernetes in a GPU-enabled stack.

Qualifications

• Depth in ML Engineering, MLOps or Data Engineering.
• Strong experience with modern ML development and deployment.
• Experience with computer vision, embedded ML or large-scale data.
• Production experience taking complex ML or data systems to real-world deployment.

Responsibilities

• Training and optimising deep learning models.
• Developing computer vision and vision-language-action architectures.
• Optimising models for constrained and embedded hardware.
• Building ML training and inference infrastructure with GPU clusters, cloud, Docker and Kubernetes.

Skills

ML Engineering

MLOps

Data Engineering

Python

C++

Rust

Tools

Docker

Kubernetes

Job description

Machine Learning Engineer (Autonomous Systems)

London | Liverpool Street | Hybrid – 3 days per week

Want to build ML that goes beyond a benchmark and actually operates in the real world?

This is an opportunity to develop and deploy machine learning for advanced autonomous systems, working with everything from computer vision and deep learning to sensor data, embedded AI and large-scale ML infrastructure.

What’s in it for you?

• Deploy ML onto real autonomous platforms
• Work with imagery,LiDAR, telemetry and sensor data
• Take models from experimentation through to real-world deployment
• Work closely with ML, hardware and systems engineers
• Tackle problems acrossdeep learning, computer vision and embedded AI
• Build systems where performance, reliability and efficiency genuinely matter

What you’ll be doing

The team is growing across ML Engineering, MLOps and Data Engineering, so the exact focus can play to your strengths.

Depending on your background, you could be:

• Training and optimising deep learning models
• Developing computer vision and vision-language-actionarchitectures
• Optimising models for constrained and embedded hardware
• Building ML training and inference infrastructureWorking withGPU clusters, cloud, Docker and Kubernetes
• Engineering large-scale geospatial and sensor datasets
• Using simulation and synthetic data to improve model performance

What you’ll bring

You don’t need to tick every box. Depth in one area is more valuable than surface-level experience across all of them.

You’ll likely have:

• Strong experience in ML Engineering, MLOps or Data Engineering
• Solid programming skills in Python, C++ or Rust
• Experience taking complex ML or data systems into production
• A good understanding of modern ML development and deployment
• Experience with computer vision, infrastructure, embedded ML or large-scale data would be particularly relevant