Job Description
We are looking for an experienced Computer Vision Engineer to help us make transport safer and greener, who thrives on solving complex problems and collaborating to drive product impact.
Salary: £55,000-£70,000k (if the advertised salary range is below your current expectations, we would still encourage you to apply. We are open to discussing the role and overall package in line with experience and scope)
Reporting to: Adam Fry (Engineering Manager, Sensor Hardware & Electronics)
Location: primarily based in our London Office, with flexible and hybrid working (Wednesdays required with 2 days per week strongly recommended).
About the role
We are looking for a Computer Vision Engineer with experience in NVIDIA’s Edge AI stack to work on our core computer vision pipelines, powering real-time inference used by authorities globally to make informed improvements to traffic systems.
In this role, you will develop and maintain the systems that power our AI traffic sensors - improving performance, accuracy, and efficiency across our GStreamer and DeepStream-based pipelines. You will work closely with both researchers and hardware engineers to ensure that models are deployed effectively and run reliably in production.
This is a highly impactful engineering role focused on deep technical expertise, where you will own key parts of our vision stack and play a central role in improving performance, scalability, and long-term evolution of our edge AI systems. Your work will substantially improve outcomes for customers, and ultimately road users around the world.
Your time will be spent roughly as follows:
- 75% - Core Engineering (pushing the boundaries of our DeepStream and GStreamer pipelines, building new features, and deploying new deep learning models)
- 15% - Reactive debugging and support
- 10% Cross-team initiatives (eg collaborating with cloud engineers on self-learning algorithms, or building dashboards for surfacing new datasets you’ve developed)
This is a unique opportunity to work at the intersection of AI, hardware, and real-world deployment - improving how thousands of sensors understand and interpret the world, and directly contributing to safer and more sustainable transport systems.
About you
You are a hands-on engineer with experience working on embedded computer vision systems and have a strong interest in how deep learning models perform in real-world environments.
You are comfortable working within existing systems and improving them over time - whether that’s optimising performance, simplifying complexity, or making systems more robust. You bring solid experience with NVIDIA’s vision stack and are confident working with GStreamer-based pipelines.
You are also open and collaborative, and excited to share your knowledge with others - including engineers from different disciplines or with different levels of experience.
Requirements for the role
- Experience working with complex and custom NVIDIA DeepStream and GStreamer-based pipelines in production (e.g. nvargus, nvinfer, nvtracker)
- Proven ability to take ownership of complex vision systems, improving structure, maintainability, and enabling upgrades (e.g. JetPack / platform upgrades)
- Experience working under edge constraints (latency, compute, memory)
- Strong understanding of end-to-end video / vision systems, from camera input (incl. Optical performance, ISPs, and tuning) through to model inference and output
Any of the following would further strengthen an application
- Proficiency with Golang
- Experience deploying deep learning models into production, especially with custom layers or kernels
- Experience with production IoT systems
- Experience with MLOps practices
- Experience working in a start-up or scale-up environment
- Interest in sustainability, transport, or smart cities
You don’t need to have done all of these things before, but to excel in this role, you will need to be keen to learn and comfortable working in a dynamic, fast-paced environment. If you’re close to what we’re looking for, please consider applying. Experience comes in many forms, skills are transferable, and passion goes a long way.
Hiring process
- 30 minute screening interview.
- 1.5 hour system design interview where you work together with a VivaCity engineer
- 1.5 hours final round interview, split into a 45 minute technical experience interview and 45 minute soft skills interview
We know that diverse ideas and perspectives drive innovation and make us better. We are creating an environment where everyone, from any background, can do their best work. We’re an equal opportunities employer and all applications will receive consideration for employment without regard to ethnicity, religion, gender, gender identity or expression, sexual orientation, nationality, disability, age, or social background. If you need any reasonable accommodations to help you perform at your best during the application process, please let us know.
About us
At VivaCity, we make cities smarter, safer, and more sustainable. We have over 5,000 AI sensors around the world, gathering real-time anonymous data on transport modes, traffic flow, and travel patterns. This is used to gather actionable insights to support strategic decisions to improve the global transport network.
All our solutions are community-centric, using ‘privacy by design’ principles. Our ultimate goal is to make the European vision of a Smart City - one which makes the city work effectively, for the community.
We pride ourselves on a collaborative, open culture that fosters innovation, learning and encourages everyone to do their best work, whilst building a sense of community and collaboration. Read more about what it’s like to work at VivaCity.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
