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- Tasks: Design and optimise ML models for secure communications on edge devices.
- Company: Exciting start-up in Oxford, linked to the University of Oxford.
- Benefits: Competitive salary, hybrid work, and a chance to shape future tech.
- Why this job: Make a real impact on AI and wireless communications that can save lives.
- Qualifications: Master's or Ph.D. in relevant fields with strong Python skills.
- Other info: Join a talented team and tackle meaningful problems in UK Defence and Security.
The predicted salary is between 60000 - 84000 £ per year.
A once in a lifetime opportunity has arisen for an Edge AI Engineer to have a major impact in the development of next generation wireless communications which will revolutionise several key industries. If you really want to contribute to future technology, and AI, Data Science, or Machine Learning is your passion, then this early stage, fast paced, and independently funded start up wants to hear from you. Led by an incredibly talented team of industry experts, and with strong links to the University of Oxford, they are on a mission to enable safe and efficient communication systems which will ultimately protect our way of life. By joining them, you the Edge AI Engineer will create a substantial impact by developing critical technology that will save lives and ensure our society remains safe in an ever-changing world.
Key responsibilities:
- Designing and optimising ML models to enhance secure communications and signal processing on a range of edge devices.
- Implementing low-latency, high-performance deep learning pipelines on hardware accelerators such as FPGA, TPU, and ASICs.
- Optimising CNN, Transformer, RNN, and/or GNN architectures for deployment on low-power embedded systems.
- Apply quantisation, pruning, distillation, and model compression to enhance efficiency.
- Strengthening model robustness against adversarial attacks and system-level security threats.
- Collaborating with embedded and security engineers to align AI performance with real-world system constraints.
Edge AI Engineer essential experience & skills:
- Master's or Ph.D. (or equivalent experience) in Data Science, Machine Learning, Artificial Intelligence, or a related field.
- Strong proficiency in Python with practical experience of PyTorch or TensorFlow.
- Working knowledge of implementing and optimising deep neural networks (e.g. CNNs, Transformers, GNNs).
- Hands-on experience with embedded C/C++ for model integration with an understanding of low-latency and low-power constraints in real-time systems.
- Awareness of adversarial ML and model robustness techniques.
- Understanding of secure-by-design principles and trusted execution concepts for AI on edge devices.
- Keenness to work on meaningful problems within the context of UK Defence and Security.
- 5+ years of experience in AI/ML systems development.
- Understanding of training-inference workflows, including data preprocessing, model evaluation and benchmarking.
- Familiarity with hardware accelerators (FPGA, TPU, ASIC, GPU-based inference).
- Experience with model optimisation techniques: quantisation, pruning, knowledge distillation and model compression.
- Proficient with Git, CI/CD and Linux-based development environments.
- Ability to document and test code for reproducibility and maintenance.
If you have experience working on Edge AI and you have a deep passion for AI, Data Science and Machine Learning, then our wireless communications start up wants to hear from you.
Edge Artificial Intelligence Engineer in Oxford employer: Mars Recruitment
Contact Detail:
Mars Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Edge Artificial Intelligence Engineer in Oxford
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even local tech events. The more you engage with others, the better your chances of landing that dream job as an Edge AI Engineer.
✨Show Off Your Skills
Don’t just talk about your experience; showcase it! Create a portfolio or GitHub repository with your projects, especially those related to AI and machine learning. This will give potential employers a taste of what you can do.
✨Reach Out Directly
If you see a role that excites you, don’t hesitate to reach out directly to the hiring manager or recruiter on LinkedIn. A friendly message expressing your interest can go a long way in making you stand out from the crowd.
✨Apply Through Our Website
Make sure to apply through our website for the best chance at getting noticed. We’re always on the lookout for passionate candidates who want to make a difference in the world of AI and wireless communications!
We think you need these skills to ace Edge Artificial Intelligence Engineer in Oxford
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Edge AI Engineer role. Highlight your experience with ML models, Python, and any relevant projects that showcase your skills in AI and data science. We want to see how you can contribute to our mission!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for AI and how it aligns with our goals at StudySmarter. Let us know why you're excited about this opportunity and how you can make an impact in the field of wireless communications.
Showcase Relevant Projects: If you've worked on projects involving deep learning pipelines or model optimisation, be sure to include them in your application. We love seeing practical examples of your work, especially those that demonstrate your problem-solving skills in real-world scenarios.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at Mars Recruitment
✨Know Your Tech Inside Out
Make sure you’re well-versed in the latest trends and technologies related to Edge AI, Machine Learning, and secure communications. Brush up on your knowledge of CNNs, Transformers, and hardware accelerators like FPGA and TPU. Being able to discuss these topics confidently will show your passion and expertise.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous projects, especially those involving low-latency systems or adversarial ML. Use the STAR method (Situation, Task, Action, Result) to structure your answers, demonstrating how you tackled complex problems effectively.
✨Collaborate Like a Pro
Since this role involves working closely with embedded and security engineers, be ready to talk about your experience in collaborative environments. Highlight any past projects where teamwork was crucial, and emphasise your ability to align AI performance with real-world constraints.
✨Ask Insightful Questions
Prepare thoughtful questions that show your interest in the company’s mission and the role. Inquire about their current projects, challenges they face in AI deployment, or how they ensure model robustness. This not only demonstrates your enthusiasm but also helps you gauge if the company is the right fit for you.
