Computer Vision Engineer

Computer Vision Engineer

Full-Time 55000 - 65000 £ / year (est.) Home office (partial)
Catapultsports

At a Glance

  • Tasks: Join us to develop innovative computer vision solutions for elite sports performance.
  • Company: Catapult, a leader in sports performance technology, transforming the industry since 2006.
  • Benefits: Collaborative culture, global team, and opportunities for personal and professional growth.
  • Other info: Dynamic environment with a focus on continuous improvement and global collaboration.
  • Why this job: Make a real impact on athletes' performance while working with cutting-edge technology.
  • Qualifications: Experience in computer vision, deep learning, and proficiency in Python and C++.

The predicted salary is between 55000 - 65000 £ per year.

Catapult is building the future of sports performance technology, with a mission to unleash the potential of every athlete and team on earth. We are actively changing the sporting industry. Since 2006, our solutions have been leading the way in sports performance software, science, and data, where 1% can literally mean the difference between winning and losing. We work with over 5,000 teams around the world, empowering coaches, managers, and trainers in premier teams in the NFL, NBA, NHL, MLS, EPL, AFL, NRL, NCAA, and more. We provide the information they need to optimise athletes’ health, game-day readiness, and performance, as well as in-game tactics.

We are looking for an enthusiastic, inquisitive, full-lifecycle Computer Vision Engineer to join our centralised, multi-disciplinary Data Science team. Our mandate is to drive platform innovation and cross-vertical reusability. Based in our London office, this role is designed for a unique technical practitioner who enjoys owning the complete lifecycle of a feature. Working collaboratively with various stakeholders, you will help translate product briefs into algorithmic solutions, train deep learning models, engineer algorithmic pipelines, and deliver optimised, production-ready deployable artifacts that power analytics used by professional sports teams and elite athletes around the world.

WHAT YOU’LL DO

  • End-to-End Pipeline Contribution: Collaborate with senior data scientists, computer vision engineers, and vertical teams to translate product requirements into practical computer vision solutions, helping design the pipeline from raw video ingestion to production inference.
  • Algorithm & Model Development: Design, train, and evaluate deep learning architectures alongside classical computer vision pipelines (e.g., feature tracking, optical flow, and spatial filtering via OpenCV).
  • Geometric Computer Vision: Develop robust mathematical pipelines for camera calibration, homography estimation, and coordinate mapping to ensure model spatial outputs are accurate and stable.
  • Modern Cloud & Containerised Deployment: Focus on architecting and containerising Python-based cloud microservices (via Docker) as our primary, future-facing deployment model.
  • Desktop Applications Support: Assist in compiling cross-platform native binaries or shared libraries linking against the ONNX Runtime C++ API to support and maintain our existing Windows/macOS desktop application footprint.
  • Automated Data Curation: Help build intelligent, automated data-ingestion pipelines that utilise model-assisted pre-labeling to continuously clean and version high-throughput training datasets.
  • Interface & Boundary Design: Participate in defining clean API boundaries and interface contracts to ensure our core data science modules integrate seamlessly into downstream vertical applications.

WHAT YOU’LL NEED

  • Core Algorithmic Background: Foundational knowledge of classical computer vision (multi-view geometry, object tracking, spatial transformation) and modern deep learning architectures (object detection, semantic/instance segmentation, transformer-based vision models).
  • Production Model Training: Experience sourcing, structuring, training, and benchmarking deep neural networks using PyTorch or TensorFlow.
  • Hybrid Language Skills: High proficiency in Python for prototyping, training, scripting, and deployment pipelines, combined with a practical capability to read, build, and debug existing C++ codebases (including exposure to build management tools like CMake).
  • Execution Graph Optimisation: Familiarity with optimising model runtimes and inference execution graphs for real-time applications using TensorRT or ONNX Runtime (e.g., quantisation, layer fusion).
  • Modern Infrastructure: Practical experience with Docker containerisation, version control (Git), and cloud platform execution (AWS).

NICE TO HAVE

  • Neural Architecture Customisation: Experience modifying, adapting, or designing custom neural network components (e.g., specialised backbones, attention mechanisms, or custom loss functions).
  • Advanced Mathematical Foundations: A strong intuitive grasp or academic background in applied linear algebra and matrix calculus, particularly as it relates to 3D spatial transformations and projective geometry.
  • Downstream Integration: Experience or familiarity with native application development tools (Visual Studio, Qt Creator) to help ease collaboration when handing off components to vertical app teams.
  • Sports Video Benchmarks: Experience experimenting with or competing in open-source sports analytics datasets and challenges (e.g., SoccerNet, SportsMOT, or similar multi-object tracking and action-spotting benchmarks).
  • Domain Alignment: A genuine interest in sports analytics, tracking technology, or elite human performance.

WHAT YOUR SUCCESS WILL LOOK LIKE

In 6 Months' Time…

  • Global Impact: Your work will be actively contributing to features underpinning informed decisions made by elite coaches and professional athletes globally.
  • Collaborative Innovation: You will have partnered with the team to take an algorithmic feature from an abstract brief to a stable, deployable Python asset.
  • Proactive Integration: You will feel completely up to speed with our workflows and comfortable actively identifying potential pipeline improvements.

In 12 Months' Time...

  • Pipeline Ownership: You will reliably manage the full lifecycle of core computer vision and data science pipelines.
  • Data-Centric Automation: You will have collaborated on the design and deployment of an automated dataset curation pipeline.

WHY CATAPULT?

We have amazing people. We promise you’ll work with some of the most ambitious, intelligent people in an exciting industry, and do some of the best work of your life. We encourage our people to engage in constructive, open, and honest communication to make Catapult extraordinary. We work in a collaborative yet challenging environment to consistently improve our performance, which in turn impacts our customers' performance. Our workforce spans more than 20 countries. You'll have the opportunity to work with people from multiple nationalities and cultures, and to build your global awareness. We value improvement and development. We are challenging ourselves to continuously grow and become a high-performance company. That means we maintain a growth mindset in everything we do, and our people are always looking for ways to improve. There is an unlimited opportunity to grow, do more, and do better.

Whether you’re interested in sports or not, you’ll have the satisfaction of knowing your work is supporting some of the most successful teams and athletes on the planet! Research shows that while men apply for jobs when they meet an average of 60% of the criteria, women and other marginalised groups tend only to apply when they check every box. So if you have what it takes, but don't meet every single point in our job ad, please still get in touch! We would love to have a chat and see if you could be a great addition to our team. We are building the future of sports performance. Our priority is to find the brightest talent who can add to our team culture, actively contribute, and be excited about what they do. All offers of employment are subject to Catapult's positive prehire check. To find out more, please contact the Talent Partner for this role.

Computer Vision Engineer employer: Catapultsports

At Catapultsports, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration in our London office. Our commitment to employee growth is evident through continuous learning opportunities and the chance to work with leading teams globally, all while contributing to groundbreaking technologies that enhance athlete performance. Join us to be part of a passionate team dedicated to making a meaningful impact in the world of sports.

Catapultsports

Contact Details:

Catapultsports Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Computer Vision Engineer

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We think you need these skills to ace Computer Vision Engineer

Computer Vision
Deep Learning
Algorithm Development
Python
C++
Docker
PyTorch

Some tips for your application 🫡

Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!

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