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- Tasks: Lead the development of advanced facial recognition and liveness detection systems.
- Company: Join a cutting-edge tech firm revolutionising security with AI.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Dynamic team environment with exciting challenges and career advancement.
- Why this job: Make a real impact in the world of AI and security technology.
- Qualifications: Experience in machine learning, computer vision, and cross-platform development.
The predicted salary is between 60000 - 80000 £ per year.
About the Company
You will own the machine learning and computer vision components of the YEO CFR SDK. This means the facial recognition model, the three-layer passive liveness detection system (software depth analysis, pixelation detection, rPPG), anti-spoofing performance, and injection attack countermeasures. You will work alongside the iOS lead (Swift/CoreML), Android lead (Kotlin/C++/MediaPipe), and Desktop lead (Kotlin Multiplatform) to optimise and deploy models across all platforms. The proprietary YEO facial recognition codebase is cross-platform portable and in production QA now. Your job is to take it from ‘working’ to ‘certified, benchmarked, and bank-ready.
About the Role
Specific responsibilities:
- Liveness detection: Own and improve the three-layer passive liveness stack. Software-based depth analysis for 3D facial structure detection. Pixelation detection for screen/print artefact identification. rPPG (remote photoplethysmography) for live pulse signal extraction from standard camera feeds.
- Anti-spoofing: Design and maintain the model pipeline to detect and reject presentation attacks: photographs, screen displays, video replays, 3D silicone masks, latex masks, and AI-generated deepfakes. Prepare the system for ISO 30107-3 PAD Level 2 certification (iBeta) and eventual Level 3.
- Injection attack detection: Build countermeasures against virtual camera injection, emulator attacks, app hooking, and synthetic video stream insertion. Implement camera integrity verification, device attestation, and runtime environment checks aligned with CEN/TS 18099.
- On-device model optimisation: Ensure all models run at inference speeds below 20ms per frame on mid-range devices (3+ years old), with battery impact below 3%, while maintaining false acceptance rate (FAR) and false rejection rate (FRR) at production-grade thresholds.
- Cross-platform deployment: Convert and optimise models for CoreML (iOS), TFLite (Android), and ONNX Runtime or equivalent (Desktop). Manage the differences in inference performance across runtimes.
- Benchmarking: Establish the performance benchmarking pipeline. Verification speed, battery impact, FAR, FRR, liveness detection accuracy, anti-spoof detection rates. Maintain benchmarks across every release.
- Certification support: Prepare the system for ISO 30107-3 PAD testing (iBeta), FIDO Face Verification, and CEN/TS 18099 IAD evaluation. Understand what the testing labs test, design the training and evaluation pipeline accordingly, and manage the certification process.
- rPPG pipeline: This is the single most technically differentiated component. You will own the rPPG signal extraction pipeline — bandpass filtering, chrominance-based methods (POS, CHROM, or equivalent), pulse signal estimation from standard RGB camera input. The objective: reliably detect a live physiological pulse signal in variable lighting, at varying distances, on devices with no depth sensor, at frame rates as low as 15fps.
Computer Vision & Machine Learning - Face Recognition Lead employer: YEO Messaging
Contact Detail:
YEO Messaging Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Computer Vision & Machine Learning - Face Recognition Lead
✨Tip Number 1
Network like a pro! Connect with folks in the industry on LinkedIn or at meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to machine learning and computer vision. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common interview questions related to facial recognition and anti-spoofing techniques to impress your interviewers.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace Computer Vision & Machine Learning - Face Recognition Lead
Some tips for your application 🫡
Show Off Your Skills: When you're writing your application, make sure to highlight your experience with machine learning and computer vision. We want to see how you've tackled similar challenges in the past, especially in areas like facial recognition and anti-spoofing.
Be Specific About Your Achievements: Don't just list your responsibilities; tell us about your achievements! Use metrics where possible to demonstrate how you improved processes or outcomes. This helps us understand the impact you've made in previous roles.
Tailor Your Application: Make sure your application speaks directly to the role. Use keywords from the job description and relate your experiences to the specific responsibilities mentioned. This shows us that you’ve done your homework and are genuinely interested in the position.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications better and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at YEO Messaging
✨Know Your Tech Inside Out
Make sure you’re well-versed in the latest advancements in computer vision and machine learning, especially around facial recognition. Brush up on concepts like liveness detection, anti-spoofing techniques, and rPPG. Being able to discuss these topics confidently will show that you're not just familiar with the tools but also passionate about the field.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous projects related to model optimisation or cross-platform deployment. Use the STAR method (Situation, Task, Action, Result) to structure your answers. This will help demonstrate your analytical thinking and how you approach complex problems.
✨Familiarise Yourself with Certification Standards
Since the role involves preparing systems for ISO 30107-3 PAD testing and other certifications, it’s crucial to understand these standards. Research what testing labs look for and be ready to discuss how you would design training and evaluation pipelines to meet these requirements.
✨Collaborate and Communicate
This position requires working closely with leads from different platforms. Be prepared to talk about your experience in collaborative environments and how you ensure effective communication across teams. Highlight any past experiences where you successfully worked with diverse tech stacks or team members.