Autonomous Systems ML Engineer: Real-World Deployment

Understanding Recruitment

City Of London

Hybrid

GBP 70,000 - 110,000

Full time

13 days ago
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Job summary

Understanding Recruitment is recruiting for an ML Engineering role focused on advanced autonomous systems in London. The position involves developing and deploying ML across real-world autonomous platforms, including computer vision, LiDAR and sensor data.

You will work within ML Engineering, MLOps and Data Engineering teams, training deep learning models, optimizing for embedded hardware, and building scalable training and inference infrastructure on GPU clusters and cloud.

Qualifications

  • 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.

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.
  • Working with GPU clusters, cloud, Docker and Kubernetes.
  • Using simulation and synthetic data to improve model performance.

Skills

ML Engineering
MLOps
Data Engineering
Python
C++
Rust
Deep Learning
Computer Vision
Embedded ML
GPU Clusters

Tools

Docker
Kubernetes
Cloud Platforms

Job description

Understanding Recruitment is recruiting for an ML Engineering role focused on advanced autonomous systems in London. The position involves developing and deploying ML across real-world autonomous platforms, including computer vision, LiDAR and sensor data.

You will work within ML Engineering, MLOps and Data Engineering teams, training deep learning models, optimizing for embedded hardware, and building scalable training and inference infrastructure on GPU clusters and cloud.

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