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- Tasks: Develop a cutting-edge Android app to revolutionise in-store shopping experiences.
- Company: Join a stealth-mode, VC-backed company focused on behavioural AI for retail.
- Benefits: Enjoy a dynamic work environment with opportunities for innovation and collaboration.
- Why this job: Be part of a mission to enhance retail with modern machine learning and mobile tech.
- Qualifications: Experience with real-time protocols and on-device machine learning is essential.
- Other info: Ideal for those passionate about behavioural data and next-gen retail solutions.
The predicted salary is between 36000 - 60000 £ per year.
About Us: We’re a VC-backed stealth-mode company building behavioural AI solutions for the retail industry. Our platform is designed from the ground up — no legacy, no patchwork systems — just a clean slate and a clear vision. Our mission is to bring the intelligence of modern machine learning directly to the in-store experience, powering real-time, context-aware interactions in physical retail environments through beautifully engineered mobile experiences.
Role: We are looking for an experienced Android Engineer to help with the development of a cutting-edge consumer facing app designed to change the physical in-store shopping experience. You’ll collaborate closely with our team of ML & backend engineers and product to deliver a mobile experience that feels fast, personalised, and deeply responsive to user context.
Responsibilities:
- Architect and implement Android features that include:
- Real-time & bi-directional communication
- On-device inference using Edge optimised frameworks
- Offline-aware & offline-first UX flows using prefetching, caching, and background sync
- Profile, optimise, and tune the app for low latency, efficient memory usage, and battery performance
- Work closely with AI & ML team members to build context-driven user experiences
- Help define and evolve a robust mobile architecture using modern patterns
- Work closely with design & product to ship polished, scalable user interactions
Essential Qualifications:
- Experience with real-time protocols (eg. WebSockets gRPC, MQTT, SSE or similar)
- Experience with implementing on-device machine learning (eg. PyTorch Mobile, ML Kit, TensorFlow Lite)
- Strong understanding of Android performance: lifecycle, coroutines, memory, and threading
- Familiarity with edge-friendly architecture: offline-first, sync queues, reactive flows
- Comfort working in fast-paced, zero-legacy, product-driven environments
- Curiosity about behavioural data, context-aware design, or next-gen retail
Nice-to-Haves:
- Knowledge and/or Experience with Bluetooth & sensor APIs
- Knowledge and/or Experience working with USB-C connected peripheral devices
- Experience in early-stage product design and MVP development
Android Engineer employer: algo1
Contact Detail:
algo1 Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Android Engineer
✨Tip Number 1
Familiarise yourself with the latest Android development tools and frameworks, especially those related to real-time communication and on-device machine learning. Being well-versed in technologies like WebSockets, gRPC, and TensorFlow Lite will give you a significant edge.
✨Tip Number 2
Showcase your understanding of performance optimisation in Android apps. Be prepared to discuss specific strategies you've used to enhance app performance, such as memory management and efficient threading, during interviews.
✨Tip Number 3
Demonstrate your ability to work collaboratively in a fast-paced environment. Highlight any past experiences where you successfully collaborated with cross-functional teams, particularly with AI, ML, or product design teams.
✨Tip Number 4
Stay updated on trends in behavioural data and context-aware design. Showing genuine curiosity and knowledge about how these concepts apply to retail can set you apart from other candidates.
We think you need these skills to ace Android Engineer
Some tips for your application 🫡
Understand the Company: Before applying, take some time to understand the company's mission and values. Familiarise yourself with their focus on behavioural AI solutions and how they aim to enhance the retail experience through technology.
Tailor Your CV: Make sure your CV highlights relevant experience, especially in Android development, real-time protocols, and on-device machine learning. Use specific examples that demonstrate your skills in these areas to make your application stand out.
Craft a Compelling Cover Letter: Write a cover letter that not only showcases your technical skills but also expresses your enthusiasm for the role and the company’s vision. Mention any relevant projects or experiences that align with their goals in the retail industry.
Showcase Your Projects: If you have worked on relevant projects, especially those involving Android apps or machine learning, include links or descriptions in your application. This will give the hiring team insight into your practical experience and problem-solving abilities.
How to prepare for a job interview at algo1
✨Showcase Your Technical Skills
Be prepared to discuss your experience with real-time protocols and on-device machine learning. Bring examples of past projects where you've implemented these technologies, as this will demonstrate your capability to handle the responsibilities of the role.
✨Understand the Company’s Vision
Research the company’s mission and how they aim to revolutionise the retail industry with AI solutions. Being able to articulate how your skills align with their goals will show your genuine interest in the position.
✨Prepare for Collaborative Scenarios
Since the role involves working closely with ML and backend engineers, think of examples where you successfully collaborated in a team setting. Highlight your communication skills and how you can contribute to a cohesive development environment.
✨Demonstrate Problem-Solving Abilities
Expect technical questions that assess your problem-solving skills, especially regarding Android performance and architecture. Be ready to discuss how you would approach optimising an app for low latency and efficient memory usage.
