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- Tasks: Develop cutting-edge recommendation systems and enhance user experiences with machine learning.
- Company: Join a fast-growing platform business revolutionising the marketplace with AI.
- Benefits: Competitive pay, hybrid work model, and opportunities for professional growth.
- Why this job: Make a real impact by applying your skills in a dynamic, collaborative environment.
- Qualifications: Experience in developing recommendation algorithms and strong Python programming skills.
- Other info: Exciting career prospects in a fast-paced, innovative company.
The predicted salary is between 36000 - 60000 ÂŁ per year.
This range is provided by Xcede. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
About the Company
Join a fast-growing platform business that connects buyers and sellers across a diverse marketplace. With deep investments in data and personalisation, the company is building advanced machine learning capabilities to improve discovery, user experience, and recommendation quality at scale. The environment is fast-moving, collaborative, and focused on bringing practical AI into live systems that support millions of transactions across unique and varied inventory types. You’ll work at the intersection of engineering, product, and data to help design recommendation infrastructure that learns and adapts in real time.
What You’ll Be Doing
- Develop recommendation systems using a blend of collaborative, content-based, and innovative techniques (reinforcement learning, LLMs, etc.) to help surface relevant results for users.
- Work on learning‑to‑rank models that fine‑tune search and browse experiences based on behavioural and contextual signals.
- Create adaptive personalisation logic based on browsing behaviour, previous interactions, and user profiles.
- Build item classification models, embeddings, and semantic search tools to enhance product discovery.
- Integrate ML services into production with support from MLOps and platform engineering teams.
- Run structured experiments including A/B tests and offline simulations to measure impact and identify optimisation opportunities.
- Apply visual similarity and image‑based features to improve ranking and content understanding where appropriate.
- Stay current with best practices and research in recommendation science, model evaluation, and user engagement.
What They’re Looking For
- Proven experience developing and deploying recommendation algorithms in a commercial setting.
- Strong Python programming ability and comfort using ML libraries like PyTorch, TensorFlow, scikit‑learn, and related tooling.
- Understanding of ranking algorithms, embeddings, and modern architectures such as two‑tower models or transformers.
- Experience working with behavioural modelling, personalisation strategies, and contextual learning.
- Familiarity with metrics used to evaluate recommender systems.
- Good understanding of cold‑start challenges, sparse data, and interaction‑based feedback loops.
- Exposure to vector search technologies and embedding‑based infrastructure is desirable.
- Comfort running experiments in production environments and interpreting results across key business and user goals.
- Bonus: experience with e‑commerce or multi‑category platforms where discovery and relevance are central to the product.
If this role interests you and you would like to find out more (or find out about other roles), please apply here or contact us via niall.wharton@Xcede.com (feel free to include a CV for review).
Machine Learning Engineer employer: Xcede
Contact Detail:
Xcede 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 projects, especially those related to recommendation systems and ML algorithms. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨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
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining our team and helps us get to know you better right from the start.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with recommendation algorithms and Python programming. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about machine learning and how you can contribute to our fast-growing platform. Keep it engaging and personal – we love to see your personality!
Showcase Your Projects: If you've worked on any cool projects involving ML libraries like PyTorch or TensorFlow, make sure to mention them. We’re keen to see practical examples of your work, especially those that relate to recommendation systems.
Apply Through Our Website: We encourage you to apply directly through our website for a smoother process. It helps us keep track of applications better and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Xcede
✨Know Your Algorithms
Brush up on your knowledge of recommendation algorithms, especially those mentioned in the job description. Be ready to discuss your experience with collaborative filtering, content-based methods, and any innovative techniques you've used, like reinforcement learning or LLMs.
✨Showcase Your Python Skills
Since strong Python programming ability is crucial for this role, prepare to demonstrate your proficiency. Bring examples of projects where you’ve used ML libraries like PyTorch or TensorFlow, and be ready to explain your coding decisions.
✨Understand the Business Impact
Be prepared to discuss how your work has impacted user engagement and business goals in previous roles. Think about specific metrics you’ve used to evaluate recommender systems and how you’ve tackled challenges like cold-start problems.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the company’s approach to machine learning and personalisation. This shows your genuine interest and helps you gauge if the company culture aligns with your values.
