Senior Computer Vision Engineer (Android)

Bring computer vision to the world of sport

What if athletes, coaches and teams could access the kind of tracking technology that has traditionally been out of reach?

We’re building a new generation of tracking services for elite and semi-professional sport, putting powerful computer vision technology directly into the hands of the people who need it most.

We’re looking for a Senior Android Computer Vision Engineer to help make that possible.

You’ll join a small, cross-functional engineering team building products at the cutting edge of sport and technology. With the direction set and plenty still to be built, you’ll have the autonomy to shape solutions, make important technical decisions and see your work make a real difference.

The role

You’ll own computer vision on the device, taking detection and tracking models and making them work reliably on consumer Android phones and tablets.

That means much more than getting a model to run. You’ll optimise and profile inference for real-world hardware, balancing performance with accuracy across different devices, lighting conditions and environments. You’ll work close to the camera capture pipeline, where frame timing, exposure and device behaviour can all affect the quality of the output.

Your challenge is to ensure that the data produced on a mobile device is accurate, reliable and trusted by the wider tracking system.

You’ll work closely with mobile, backend, computer vision and machine learning engineers, with the freedom to own both your technical approach and its implementation. We also embrace AI-augmented and AI-delegated development as part of how we work, enabling a small team to build and iterate at pace.

This is an opportunity to solve genuinely challenging engineering problems, influence how on-device vision is built and help establish the foundations for a technology that can change how sport is experienced and analysed.

What you’ll be doing

• Own on-device inference: Design and implement Android inference solutions that meet demanding accuracy, latency and performance requirements.
• Build the native Android vision layer: Develop and maintain C++ components using the Android NDK, alongside JNI bindings that connect them to our Kotlin application.
• Optimise models for mobile: Convert, quantise, profile and optimise detection and tracking models, exploring techniques such as pruning, graph optimisation and operator-level improvements.
• Solve real-world camera challenges: Work close to the capture pipeline, understanding how frame timing, exposure and camera behaviour influence model performance.
• Make accuracy measurable: Build validation tools and measurement processes that demonstrate performance against ground truth in real-world conditions.
• Engineer for real devices: Account for thermal constraints, battery consumption, hardware differences and the unpredictable conditions encountered in the field.
• Collaborate on model improvements: Work with our computer vision and machine learning specialists to identify, prototype and evaluate improvements to model performance.
• Shape technical decisions: Help determine what should run on-device and what should run elsewhere, bringing clear reasoning and an understanding of technical trade-offs.
• Build production-quality software: Write maintainable, testable and reliable code that meets the team's engineering standards.
• Work across disciplines: Communicate clearly with mobile application, backend and machine learning engineers who depend on your work.
• Build with AI: Use AI-assisted and delegated development tools as a standard part of your workflow, working within our security and intellectual property guidelines.

As the team grows, there may also be opportunities to mentor other engineers, take technical leadership of on-device vision and establish the standards and practices that shape our approach to edge AI.

Our technology

Our stack includes C++, Android NDK, JNI, Kotlin, Python and AWS, supported by Git, GitHub Actions, Jira and Android mobile inference and profiling tools.

What you’ll bring

You’re an experienced computer vision or machine learning engineer who enjoys taking algorithms beyond the lab and making them work reliably in production.

You’re comfortable working close to the hardware, investigating problems that don’t show up in controlled environments and making informed trade-offs between accuracy, performance and resource consumption. You can work independently, communicate your thinking clearly and collaborate with engineers across different disciplines.

You’ll ideally bring:

• At least 4 years of applied computer vision or machine learning engineering experience, including solutions deployed into production.
• Demonstrable experience running inference on Android or embedded devices, rather than working exclusively with desktop or server hardware.
• A degree in computer science, engineering, mathematics or a related discipline, or equivalent practical experience.
• A strong interest in learning new technologies, tools and platforms.

Experience in automotive, embedded systems or other resource-constrained environments could be particularly relevant.

You’ll have expert-level knowledge of:

• Applied computer vision, including object detection, tracking, camera geometry and the challenges that arise outside controlled environments.
• Mobile or embedded inference runtimes, including model conversion and quantisation toolchains.
• Model optimisation and performance profiling for resource-constrained hardware.
• C++, particularly the Android NDK and JNI.
• Measuring and validating model accuracy against ground truth.

You’ll also be comfortable with:

• Kotlin, with enough experience to work effectively across the JNI boundary.
• Python for prototyping, tooling and data analysis.
• Mobile camera capture pipelines, including frame timing and exposure control.
• Machine learning fundamentals, allowing you to contribute meaningfully to discussions about model improvements.
• Git, code reviews and continuous integration practices.

It would be beneficial to understand:

• Real-time and latency-sensitive systems.
• Spatial computing and multi-view reconstruction.
• The Android application development and release lifecycle.

You don’t need to have worked with every technology listed. We’re interested in the depth of your engineering experience, your ability to solve difficult problems and your willingness to learn.

Hungary Benefits

• Annual Leave: Standard according to local labour law
• Flexible Working: Hybrid model (2 days in our office per week)
• Access to sporting events and tickets
• Sony Group Company Discounts
• Other Benefits: Cafeteria options (SZÉP Card, Nursery & Kindergarten contributions, Annual MOL Bubi Pass), Private Medical Insurance, Life & Accident Insurance, MindentMent Dental Clinic discounts, Seniority Awards, Employee Assistance Program

About Hawk-Eye Innovations

Hawk-Eye Innovations is at the forefront of sports technology, delivering officiating, tracking, broadcast and performance solutions across some of the world's biggest sports. As part of Sony Sports, we continue to push the boundaries of innovation, helping athletes, coaches, teams, broadcasters and governing bodies make better decisions through technology.

Why Hawk-Eye?

At Hawk-Eye, you'll have the opportunity to work on technology used across the world's leading sports while solving complex technical challenges alongside talented engineers and product teams. You'll play a key role in shaping products from the ground up, working in an environment that values innovation, collaboration and continuous learning.

If you're excited by the opportunity to build cutting-edge mobile products that make a genuine impact in sport, we'd love to hear from you.