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- Tasks: Develop and deploy cutting-edge algorithms for real-time decision-making in space applications.
- Company: Join a pioneering UK startup transforming satellite operations with advanced technology.
- Benefits: Enjoy flexible hybrid work, competitive salary, equity options, and wellness stipends.
- Why this job: Work on groundbreaking tech at the intersection of AI and space, shaping the future of exploration.
- Qualifications: PhD or equivalent experience in Computer Vision, AI, or Robotics; strong portfolio required.
- Other info: Collaborate closely with engineers and contribute to a vibrant company culture.
The predicted salary is between 36000 - 60000 £ per year.
A significant on-site presence (expected to be ~4 days per week) is essential for this role to foster collaboration with the engineering team and build our foundational culture.
About the Role:
The SpaceLead is seeking a top-tier Computer Vision Engineer for a UK startup backed by leading institutions. Our client is developing advanced technology to enable next-generation in-orbit services and satellite operations. You will be responsible for developing and deploying the core perception algorithms that enable real-time, autonomous decision-making on resource-constrained hardware for space applications. This is a rare opportunity to solve first-of-their-kind problems at the intersection of AI and space, contributing directly to a groundbreaking platform.
What You'll Do:
- Design, train, and deploy deep learning models for real-time object detection, segmentation, pose estimation, and tracking on space-based sensor data.
- Optimize neural network architectures for efficient deployment on low-power, resource-constrained embedded systems.
- Develop and manage robust data pipelines for collecting, augmenting, and labeling sensor data from unique operational environments.
- Collaborate closely with hardware and systems engineers to co-design software/hardware solutions for maximum performance.
- Create rigorous testing and validation frameworks to ensure model accuracy and reliability under challenging orbital conditions.
- Drive research into state-of-the-art techniques and apply them to solve novel challenges in space-based perception.
What You'll Need:
- A PhD (or equivalent industry research experience) in Computer Science, Aerospace Engineering, or a related field with a focus on Computer Vision, AI, or Robotics.
- A strong portfolio of relevant projects, publications (e.g., CVPR, ICCV, NeurIPS), or patents.
- Demonstrated experience or a strong academic background in aerospace, satellite systems, or robotics for extreme environments.
- Direct, hands-on experience with model optimization for edge devices (e.g., quantization, pruning) and deployment on low-power embedded hardware.
- Deep expertise in computer vision principles, including image processing, feature detection, and 3D geometry.
- Strong proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow).
- Excellent problem-solving skills and the ability to work on complex technical challenges.
- Eligibility to work in the UK.
What Our Client Offers:
- A competitive salary and meaningful equity package.
- The opportunity to work on groundbreaking technology for the space sector.
- Flexible, hybrid working arrangements.
- A generous budget for professional development and a wellness/lifestyle stipend.
- A key role in shaping company culture and strategy from the ground up.
- Private health insurance and a statutory pension scheme.
To Apply:
Please submit your application directly via LinkedIn. All applications are managed confidentially by The SpaceLead on behalf of our client.
About The SpaceLead:
The SpaceLead partners with innovative companies shaping the future of space. To stay ahead in the sector, follow our LinkedIn Newsletter for insights on space, autonomy, and deep tech.
Computer Vision Engineer - Edge AI employer: The SpaceLead
Contact Detail:
The SpaceLead Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Computer Vision Engineer - Edge AI
✨Tip Number 1
Make sure to showcase your hands-on experience with model optimisation for edge devices. Highlight any specific projects where you've successfully implemented techniques like quantisation or pruning, as this is crucial for the role.
✨Tip Number 2
Network with professionals in the aerospace and computer vision fields. Attend relevant conferences or webinars, and engage with experts on platforms like LinkedIn to increase your visibility and learn about potential opportunities.
✨Tip Number 3
Familiarise yourself with the latest research and advancements in space-based perception. Being able to discuss state-of-the-art techniques during interviews will demonstrate your passion and commitment to the field.
✨Tip Number 4
Prepare to discuss your collaborative experiences with hardware and systems engineers. This role requires a strong team dynamic, so sharing examples of successful teamwork will help you stand out as a candidate.
We think you need these skills to ace Computer Vision Engineer - Edge AI
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in computer vision, AI, and robotics. Include specific projects or publications that demonstrate your expertise in these areas, especially those related to space applications.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for the role and the company. Mention how your skills align with the job requirements, particularly your experience with deep learning models and edge devices.
Showcase Your Portfolio: If you have a portfolio of projects, include links or references to it in your application. Highlight any work that involves model optimisation for low-power hardware or innovative solutions in extreme environments.
Highlight Collaboration Skills: Since the role requires close collaboration with hardware and systems engineers, emphasise any past experiences where you successfully worked in a team setting to solve complex technical challenges.
How to prepare for a job interview at The SpaceLead
✨Showcase Your Projects
Prepare to discuss your portfolio in detail. Highlight specific projects related to computer vision, especially those involving deep learning models and edge AI. Be ready to explain your role, the challenges you faced, and how you overcame them.
✨Understand the Hardware
Since this role involves deploying algorithms on resource-constrained hardware, brush up on the specifics of low-power embedded systems. Familiarise yourself with model optimisation techniques like quantisation and pruning, and be prepared to discuss how you've applied these in past projects.
✨Collaborative Mindset
Emphasise your ability to work closely with hardware and systems engineers. Prepare examples of how you've collaborated in the past to co-design software/hardware solutions, as teamwork is crucial for success in this role.
✨Stay Current with Research
Demonstrate your knowledge of state-of-the-art techniques in computer vision and AI. Be ready to discuss recent advancements or papers that have inspired your work, showing that you're engaged with the latest developments in the field.
