Build the Intelligence Behind Next Generation Robotics
Industrial robotics is evolving rapidly, but most robotic systems still struggle with fragmented integrations, unreliable perception, and limited scalability in real world environments.
Our client is changing that.
This UK based robotics company is building a next generation robotics platform designed to simplify deployment, improve operational intelligence, and accelerate automation across manufacturing and logistics environments. By combining robotics, machine learning, software engineering, and control systems, the company is creating robot agnostic technologies that transform industrial automation into a scalable, productised platform.
As the business continues to grow, they are now looking for an Applied Scientist / ML Engineer to help develop the machine learning systems powering the next generation of intelligent robotics.
This is an opportunity for someone who enjoys taking cutting edge machine learning research and turning it into reliable, production grade systems that operate in demanding real world environments.
The Role
The successful candidate will play a critical role in designing, training, deploying, and optimising machine learning models used within advanced robotics applications.
Working at the intersection of computer vision, robotics, and AI, they will develop deep learning systems that allow robots to perceive environments, understand scenes, process sensor inputs, and operate with greater reliability and precision.
The company is particularly interested in individuals who can think beyond incremental improvements and apply modern machine learning techniques to solve complex industrial challenges. This includes leveraging foundation models, generative AI approaches, synthetic data generation, and scalable ML infrastructure to improve robotics capability and deployment speed.
A key responsibility will be developing robust neural network architectures focused on object detection, segmentation, pose estimation, and scene understanding. The successful candidate will work on models that directly impact robotic perception and decision making in live industrial environments.
The role will also involve building scalable training and inference pipelines capable of supporting high performance production workloads. This includes fine tuning models, optimising inference performance, improving system reliability, and ensuring models can operate effectively in real time robotics applications.
In addition, the Applied Scientist / ML Engineer will contribute to the design and maintenance of data pipelines covering collection, ingestion, curation, versioning, and synthetic data generation. The ability to create scalable and maintainable ML workflows will be highly valuable as the company continues to expand its robotics platform.
The successful candidate will work closely with robotics and software engineering teams to integrate machine learning systems into broader robotics pipelines. This includes working with distributed GPU training systems, cloud infrastructure, and edge deployment environments.
Beyond technical delivery, the company is looking for someone who values strong engineering fundamentals and understands how to build production ready ML systems rather than isolated research prototypes. Testing, modular design, benchmarking, and maintainability will all be important parts of the role.
What They’re Looking For
The ideal candidate will have a strong background in applied machine learning, computer vision, or robotics engineering, alongside experience building production ML systems.
Candidates should have:
• A PhD in Machine Learning, Robotics, Computer Vision, or a related field, or 2 to 5+ years of relevant industry experience
• Strong experience with deep learning and computer vision techniques
• A solid understanding of probability, optimisation, statistics, and ML fundamentals
• Strong Python programming skills, with C++ experience considered highly beneficial
• Experience using frameworks such as PyTorch, TensorFlow, or JAX
• Experience building scalable ML systems for production environments
• Familiarity with cloud or GPU based training infrastructure and MLOps workflows
• Experience designing evaluation, testing, and benchmarking frameworks
• Hands on experience with object detection, segmentation, pose estimation, camera calibration, and sensor integration
Additional experience with GPU acceleration, edge deployment, ROS/ROS2, Docker, CI/CD pipelines, or robotics integration would be highly advantageous.
Why Join?
This is an opportunity to join a business building genuinely innovative robotics technology with the potential to transform industrial automation on a global scale.
The role offers exposure to complex engineering challenges across machine learning, robotics, computer vision, and distributed systems while working alongside a highly technical team developing real world robotics products.
For ML engineers and applied scientists who want to move beyond pure research and build intelligent systems that directly power physical robotics platforms, this is an opportunity to make a tangible impact in a rapidly growing space.