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For Companies At a Glance
- Tasks: Build and optimise scalable ML systems for hyper-personalised retail experiences.
- Company: VC-backed startup revolutionising retail with cutting-edge technology.
- Benefits: Competitive salary, flexible work hours, and opportunities for professional growth.
- Why this job: Join a mission to transform retail through innovative machine learning solutions.
- Qualifications: 3-5+ years in ML systems, strong coding skills, and cloud experience.
- Other info: Dynamic startup environment with mentorship and career advancement opportunities.
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).
- Establish engineering best practices for ML development, including testing, CI/CD, and code review standards.
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)
Machine Learning Engineer employer: algo1
Contact Detail:
algo1 Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with fellow Machine Learning enthusiasts. 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 demonstrate your ability to build scalable systems. Share it on platforms like GitHub and make sure it's easy for potential employers to see what you can do.
✨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 and approach real-world problems!
✨Tip Number 4
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 mission to revolutionise retail with hyper-personalised experiences.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with ML systems and software engineering in your application. We want to see how you've tackled challenges in the past, so don’t hold back on those impressive projects!
Tailor Your Application: Take a moment to customise your CV and cover letter for this role. Mention specific technologies and methodologies from the job description that you’ve worked with. It shows us you’re genuinely interested and have done your homework!
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language and avoid jargon unless it's relevant. We appreciate a well-structured application that gets straight to the point!
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 makes the whole process smoother for everyone involved.
How to prepare for a job interview at algo1
✨Know Your ML Fundamentals
Brush up on your machine learning fundamentals before the interview. Be ready to discuss model training, evaluation, and common architectures. This will show that you have a solid understanding of the core concepts that are crucial for the role.
✨Showcase Your Software Engineering Skills
Prepare to demonstrate your software engineering skills during the interview. Bring examples of clean code, testing practices, and debugging techniques you've used in past projects. This will highlight your ability to build robust and maintainable ML systems.
✨Familiarise Yourself with Their Tech Stack
Research the specific ML frameworks and cloud platforms mentioned in the job description, like PyTorch, TensorFlow, AWS, or GCP. Being able to discuss your experience with these tools will make you stand out as a candidate who is ready to hit the ground running.
✨Prepare Questions About Their Projects
Think of insightful questions about their current ML infrastructure and projects. Asking about their approach to optimising performance and scalability shows your genuine interest in the role and helps you understand how you can contribute effectively.
