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- Tasks: Build and optimise scalable ML systems for hyper-personalised retail experiences.
- Company: VC-backed startup revolutionising retail with innovative technology.
- Benefits: Competitive pay, equity options, and a dynamic work environment.
- Why this job: Join us to shape the future of retail through cutting-edge machine learning.
- Qualifications: 3-5+ years in ML systems, strong software engineering skills, and cloud experience.
- Other info: Collaborative culture with exciting challenges and growth opportunities.
The predicted salary is between 36000 - 60000 £ per year.
We are a VC-backed startup focused on hyper-personalisation, currently in stealth. Inspired by the latest in recommender systems, we leverage transformers and graph learning alongside decision-making models to build the most engaging customer experiences for in-store retail.
Our mission is to change retail forever through hyper-personalised experiences that are both simple and beautiful.
We are looking for a Machine Learning Engineer with strong software engineering fundamentals to join our team of domain experts and researchers. You will be responsible for building robust, scalable ML systems that bring our foundation models for retail from prototype to production.
- Design and build production-grade ML infrastructure, including training pipelines, model serving, and monitoring systems.
- Collaborate with research engineers to translate experimental models into reliable, maintainable software.
- Optimise ML systems for performance, scalability, and cost-efficiency in cloud environments (distributed clusters, GPUs).
- Establish engineering best practices for ML development, including testing, CI/CD, and code review standards.
Month 1: Onboard to existing ML codebase and infrastructure; ship incremental improvements to model serving latency or pipeline robustness.
Month 6: Lead the end-to-end productionisation of our foundation model, meeting latency, throughput, and reliability SLAs.
Requirements:
- 3–5+ years building and maintaining ML systems in production environments.
- BSc or MSc in Computer Science, Software Engineering, or a related field.
- Strong software engineering skills: clean code, testing, debugging, version control, and system design.
- Proficiency in Python with experience in ML frameworks (PyTorch, TensorFlow, or JAX).
- Hands-on experience with cloud platforms (AWS, GCP, or Azure) and containerisation (Docker, Kubernetes).
- Solid understanding of ML fundamentals (model training, evaluation, common architectures).
Desired Skills (Bonus Points):
- Building data pipelines (real-time or batch) using tools like Apache Spark, Kafka, Airflow, or dbt.
- Familiarity with recommender systems, transformers, or graph neural networks.
A dynamic, collaborative work environment with cutting-edge ML challenges. Competitive compensation and equity in a rapidly growing company.
Machine Learning Engineer (hybrid or remote) in London employer: algo1
Contact Detail:
algo1 Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (hybrid or remote) in London
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even online forums. The more you engage with others, the better your chances of landing that Machine Learning Engineer role.
✨Show Off Your Skills
Create a portfolio showcasing your projects and contributions to ML systems. Whether it's GitHub repos or personal projects, let your work speak for itself. This is your chance to shine and demonstrate your expertise!
✨Ace the Interview
Prepare for technical interviews by brushing up on your coding skills and ML concepts. Practice common interview questions and be ready to discuss your past experiences. Confidence is key, so show them what you've got!
✨Apply Through Our Website
Don't forget to apply directly through our website! It shows you're genuinely interested in joining our team and makes it easier for us to spot your application. Let's make this happen together!
We think you need these skills to ace Machine Learning Engineer (hybrid or remote) in London
Some tips for your application 🫡
Show Your Passion for ML: When writing your application, let us see your enthusiasm for machine learning! Share any personal projects or experiences that highlight your skills and interest in the field. We love seeing candidates who are genuinely excited about what they do.
Tailor Your CV and Cover Letter: Make sure to customise your CV and cover letter for the Machine Learning Engineer role. Highlight relevant experience, especially with ML systems and software engineering. We want to see how your background aligns with our mission of hyper-personalisation in retail.
Be Clear and Concise: Keep your application clear and to the point. Use bullet points where possible to make it easy for us to read through your qualifications. We appreciate straightforward communication, so don’t be afraid to show off your skills without fluff!
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’re considered for the role. Plus, it shows us you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at algo1
✨Know Your ML Fundamentals
Brush up on your machine learning fundamentals, especially around model training and evaluation. Be ready to discuss common architectures and how they apply to real-world scenarios, particularly in retail.
✨Showcase Your Software Skills
Prepare to demonstrate your software engineering skills. Bring examples of clean code, testing practices, and debugging techniques you've used in past projects. They’ll want to see your proficiency in Python and any ML frameworks you’ve worked with.
✨Familiarise with Their Tech Stack
Research the cloud platforms and containerisation tools mentioned in the job description. If you have experience with AWS, GCP, or Azure, be ready to discuss specific projects where you utilised these technologies.
✨Prepare for Collaborative Scenarios
Since collaboration is key, think of examples where you’ve worked with others to translate experimental models into production-ready systems. Highlight your experience with CI/CD and how you’ve established best practices in previous roles.
