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For Companies At a Glance
- Tasks: Design and build 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: Make a real impact on millions of shopping experiences with cutting-edge ML.
- Qualifications: 3-5 years in ML systems, strong software engineering skills, and Python proficiency.
- Other info: Collaborative culture with opportunities for ownership and career growth.
The predicted salary is between 36000 - 60000 £ per year.
About Us
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.
About the Job
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.
Key Responsibilities
- 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).
Progression Timeline
- Month 1: Onboard to existing ML codebase and infrastructure; identify technical debt and reliability gaps; ship incremental improvements to model serving latency or pipeline robustness.
- Month 3: Own and deliver a major infrastructure component (e.g., feature store, training orchestration, or model registry); improve system observability with logging, metrics, and alerting.
- Month 6: Lead the end-to-end productionisation of our foundation model, meeting latency, throughput, and reliability SLAs; mentor teammates on engineering standards and contribute to architectural decisions.
Essential Qualifications
- 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)
- Experience with MLOps tooling (MLflow, Kubeflow, Weights & Biases, or similar)
- 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
- Exposure to model optimisation techniques (quantisation, distillation, efficient inference)
What We Offer
- Opportunity to build technology that will transform millions of shopping experiences.
- Real ownership and impact in shaping product and company direction.
- A dynamic, collaborative work environment with cutting-edge ML challenges.
- Competitive compensation and equity in a rapidly growing company.
Machine Learning Engineer in England employer: algo1
Contact Detail:
algo1 Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in England
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those that highlight your experience with production systems. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your past projects in detail. We want to see how you think!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our mission to revolutionise retail.
We think you need these skills to ace Machine Learning Engineer in England
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: Make sure your CV is tailored to the role of Machine Learning Engineer. Highlight relevant experience, especially with ML systems in production. We want to see how your background aligns with our mission to change retail through hyper-personalisation.
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. We appreciate well-structured applications that get straight to the important bits without unnecessary fluff.
Apply Through Our Website: Don’t forget to apply 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 you’re serious about 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 optimisation techniques, as these are likely to come up during the interview.
✨Showcase Your Software Skills
Prepare to demonstrate your strong software engineering skills. Bring examples of clean code, testing practices, and debugging experiences. They’ll want to see how you approach system design and version control, so have some projects in mind that highlight these abilities.
✨Familiarise with Their Tech Stack
Research the specific ML frameworks and cloud platforms mentioned in the job description. If you have experience with PyTorch, TensorFlow, or any cloud services like AWS or GCP, be ready to discuss how you've used them in past projects.
✨Prepare for Collaboration Questions
Since collaboration is key in this role, think about times you've worked with research engineers or cross-functional teams. Be prepared to share how you translated experimental models into production-ready systems and how you’ve contributed to engineering best practices.
