Machine Learning Engineer - Computer Vision & Deep Learning

Machine Learning Engineer - Computer Vision & Deep Learning

Salary: £60,000 to £95,000, depending on experience

Location: West London

Working arrangement: Predominantly office and customer-site based, with some remote working available

Employment: Permanent, full-time

MFK Recruitment is recruiting Machine Learning Engineers for an innovative UK technology company developing advanced Artificial Intelligence and Machine Learning systems for defence, security and other demanding real-world environments.

We are recruiting at junior, mid-level and senior levels. Applications are welcomed from talented Machine Learning Engineers at different stages of their careers, with responsibilities and salary determined by experience and technical ability.

These positions are specifically focused on computer vision, Convolutional Neural Networks and Transformer-based vision models. They are not general Data Science, NLP or LLM vacancies.

The company develops technology across computer vision, machine perception and autonomous systems, translating advanced AI research into reliable products that operate in complex real-world environments.

MFK Recruitment has successfully recruited four Engineers to this company over the past five years, and all four remain with the business. This reflects the quality of the technical work, company culture and long-term opportunities available.

Machine Learning Engineer role

As a Machine Learning Engineer, you will design, train, evaluate and deploy deep-learning models for computer vision applications.

Your work will centre on CNNs, Vision Transformers and related architectures for areas such as object detection, tracking, segmentation, classification, localisation and visual perception.

This is a hands-on position combining machine-learning research, model development and production software engineering. You will work with complex image and video datasets and help turn recent computer-vision research into accurate, robust and deployable systems.

The company is building its team across junior, mid-level and senior levels:

• Junior Engineers will work alongside experienced colleagues, developing their skills while contributing to real-world computer-vision projects.
• Mid-level Engineers will take ownership of model development, experimentation and production delivery.
• Senior Engineers will lead complex technical work, influence architecture decisions and help mentor other members of the team.

Responsibilities

• Design and develop deep-learning models for computer vision applications.
• Build and optimise CNN and Vision Transformer architectures.
• Develop models for object detection, tracking, segmentation, classification, pose estimation or related perception tasks.
• Design and adapt neural-network architectures, loss functions and training strategies.
• Build reliable data-processing, training, validation and evaluation pipelines.
• Prepare and manage image and video datasets for model development.
• Define appropriate evaluation metrics and analyse model performance.
• Implement and adapt techniques from recent computer-vision and deep-learning research.
• Optimise models for accuracy, robustness, latency and computational efficiency.
• Translate research prototypes into maintainable, production-quality software.
• Support the deployment of models across cloud, desktop and edge-computing environments.
• Work closely with Machine Learning Engineers, Software Engineers, Researchers, Technical Leaders and customers.
• Contribute to architecture decisions, technical planning, testing, code reviews and CI/CD practices.

The level of ownership and technical leadership expected will reflect the successful candidate’s experience.

Essential experience

• Practical experience developing deep-learning solutions for computer vision through commercial work, academic research or substantial personal projects.
• Strong understanding of Convolutional Neural Networks.
• Experience working with Transformer architectures for computer vision or multimodal perception.
• Experience using PyTorch. Strong TensorFlow experience may also be considered.
• Strong Python development skills.
• Experience with areas such as object detection, tracking, segmentation, classification or pose estimation.
• Experience training, validating and evaluating deep-learning models.
• Understanding of neural-network architectures, loss functions, hyperparameter optimisation and model-performance evaluation.
• Experience working with image, video or sensor datasets.
• An interest in implementing techniques from recent machine-learning and computer-vision research.
• Good software-engineering practices, including Git, testing and maintainable code.
• Strong analytical, problem-solving and communication skills.

Desirable experience

• Real-time computer vision and video analytics.
• Multi-object tracking, multi-camera systems or multi-modal perception.
• C++ development.
• CUDA and GPU optimisation.
• TensorRT, ONNX Runtime, OpenVINO or similar inference-optimisation technologies.
• NVIDIA Jetson or other edge-AI platforms.
• Docker, Kubernetes, Kubeflow or production MLOps pipelines.
• Distributed training or large-scale model optimisation.
• Synthetic-data generation, Unreal Engine or NVIDIA Omniverse.
• Aerial, satellite, drone or ISR imagery.
• Sensor fusion, autonomy or robotics.
• Defence, government or national-security projects.
• A BSc, MSc or PhD in Computer Vision, Artificial Intelligence, Machine Learning, Robotics, Computer Science or a related subject.

Security clearance

Candidates must be eligible to obtain UK BPSS and Security Check clearance. Existing clearance would be advantageous but is not essential for every appointment.

Applicants for SC clearance are normally expected to have lived in the UK for the previous five years. A shorter period of UK residency or time spent overseas will not automatically prevent clearance, but eligibility will be assessed individually.

The positions are predominantly based at the company’s West London office and customer sites, with some remote working available.