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- Tasks: Build scalable ML systems and optimise performance for retail experiences.
- Company: VC-backed startup revolutionising retail with hyper-personalisation.
- Benefits: Competitive pay, equity options, and a hybrid remote work model.
- Why this job: Join a dynamic team tackling cutting-edge ML challenges in a fast-paced environment.
- Qualifications: 3-5 years in ML systems, strong software engineering skills, and Python proficiency.
- Other info: Collaborative culture with opportunities for professional growth and innovation.
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 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 Remote in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues 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 projects, especially those related to ML systems. Share your GitHub link when you apply through our website; it’ll give us a taste of what you can do!
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and ML fundamentals. Practice common algorithms and system design questions, and don’t forget to review your past projects to discuss them confidently.
✨Tip Number 4
Follow up after interviews! A quick thank-you email can go a long way in keeping you top of mind. It shows your enthusiasm and professionalism, which we definitely appreciate at StudySmarter.
We think you need these skills to ace Machine Learning Engineer - Hybrid 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 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 this role. Highlight relevant experience with ML systems, cloud platforms, and any specific tools mentioned in the job description. We want to see how your background aligns with our mission!
Be Clear and Concise: Keep your application straightforward and to the point. Use clear language and avoid jargon unless it's relevant to the role. We appreciate candidates who can communicate complex ideas simply and effectively.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it helps us keep everything organised on our end.
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 familiarity with ML frameworks like PyTorch or TensorFlow.
✨Familiarise with Cloud Platforms
Since the role involves cloud environments, make sure you’re comfortable discussing AWS, GCP, or Azure. Have examples ready of how you've used these platforms for deploying ML systems, including any experience with containerisation tools like Docker or Kubernetes.
✨Collaborate and Communicate
Emphasise your ability to collaborate with research engineers and other team members. Prepare to discuss how you’ve translated experimental models into production-ready software and established best practices in previous roles.
