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For Companies At a Glance
- Tasks: Lead a team of ML Scientists to innovate product recommendations and improve user engagement.
- Company: Join a leading digital marketplace focused on sustainable ecommerce with 35 million users.
- Benefits: Enjoy a competitive salary, private health coverage, flexible working, and generous leave policies.
- Why this job: Make a real impact in ecommerce while working with cutting-edge machine learning technologies.
- Qualifications: 7+ years in machine learning and 2+ years in leadership roles required.
- Other info: Dynamic work environment with opportunities for professional growth and development.
The predicted salary is between 84000 - 196000 £ per year.
About The Company
Our client is an extremely well-known digital marketplace focused on sustainable ecommerce. With over 35 million active users globally, they’re redefining how people buy and sell second-hand fashion, aiming to make the future of style both circular and accessible. The company has offices in the UK, EU, and US and has experienced significant growth, especially around the US market, and now operates as part of a leading global e-commerce group. They pride themselves on fostering inclusivity, creativity, and innovation, values that extend to both their community and their teams. The organisation champions diversity, equal opportunity, and flexible working. They offer a progressive benefits package designed to support wellbeing, learning, and work-life balance.
The Role of Engineering Manager, Machine Learning, Marketplace, Ecommerce
Our client is seeking an experienced Machine Learning Engineering Manager to lead the recommendation team. This person will drive innovation in how users find and engage with products through advanced machine learning models, improving search relevance, personalisation, and conversion outcomes at scale. You’ll lead a talented team of ML Scientists and collaborate cross-functionally with product, data, and engineering leaders to define and execute the ML roadmap for search. This is an opportunity to have tangible business impact while working with cutting-edge technology in NLP, computer vision, and multimodal retrieval.
Key Responsibilities for the Engineering Manager, Machine Learning, Marketplace, Ecommerce
- Lead, coach, and develop a team of ML Scientists, fostering a culture of experimentation, collaboration, and continuous learning.
- Partner with Product, Data, and Engineering leaders to shape and deliver an actionable ML strategy that drives engagement, conversion, and growth.
- Oversee the design, training, and deployment of search and recommendation models — from data strategy to monitoring and performance optimisation.
- Collaborate with platform and MLOps teams to ensure robust, efficient, and scalable ML workflows (including CI/CD, feature management, and monitoring).
- Share insights and best practices across other ML teams, particularly in areas of recommendations, ranking, and multimodal representation learning.
- Stay current with emerging research in NLP, CV, and multimodal retrieval; champion responsible AI principles; and communicate findings to both technical and non-technical audiences.
Requirements for the role
- 7+ years of experience in applied machine learning with a proven track record delivering production models at scale.
- At least 2 years of leadership experience managing ML Scientists or Engineers.
- Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms).
- Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow.
- Strong grounding in experimental design, A/B testing, and the use of offline/online metrics to guide product strategy.
- Excellent communication and stakeholder management skills, with the ability to bridge complex ML concepts for diverse audiences.
- Familiarity with AWS and Databricks.
- Experience with search infrastructure (e.g., OpenSearch or Elasticsearch).
What can they offer you
- Private health and mental wellbeing coverage, including access to counselling and coaching.
- Salary of up to £140,000 + Bonus & Benefits.
- 25 days annual leave, plus additional company-wide rest days and volunteer leave.
- Flexible hybrid working, with the option to work abroad for limited periods.
- Generous parental, IVF, and carer leave policies.
- Learning and development budgets for conferences, mentorship, and skills growth.
- Pension matching, life insurance, and recognition for service milestones.
If you are interested in this position, please drop over your CV and we will give you a call if we think you are a good fit!
Engineering Manager, Machine Learning, Marketplace, Ecommerce, | 35 Million Users | UK Remote O[...] in Ashton-under-Lyne employer: Jobster
Contact Detail:
Jobster Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Engineering Manager, Machine Learning, Marketplace, Ecommerce, | 35 Million Users | UK Remote O[...] in Ashton-under-Lyne
✨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
Prepare for those interviews! Research the company, understand their products, and be ready to discuss how your experience aligns with their goals. Practice common interview questions and have your own questions ready to show your interest.
✨Tip Number 3
Showcase your skills! If you’ve worked on relevant projects, create a portfolio or GitHub repository to demonstrate your expertise. This is especially important for technical roles like Engineering Manager in Machine Learning.
✨Tip Number 4
Don’t forget to 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 take that extra step to engage with us directly.
We think you need these skills to ace Engineering Manager, Machine Learning, Marketplace, Ecommerce, | 35 Million Users | UK Remote O[...] in Ashton-under-Lyne
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the specific skills and experiences that match the Engineering Manager role. Highlight your leadership experience and any relevant machine learning projects you've worked on, especially those related to search and recommendation systems.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about sustainable ecommerce and how your background aligns with our mission. Share specific examples of how you've driven innovation in previous roles, particularly in machine learning.
Showcase Your Technical Skills: Don’t forget to mention your hands-on experience with tools like Python, Spark, and frameworks such as PyTorch or TensorFlow. We want to see how you’ve applied these in real-world scenarios, so be specific!
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!
How to prepare for a job interview at Jobster
✨Know Your Machine Learning Stuff
Make sure you brush up on your machine learning concepts, especially around search and recommendation systems. Be ready to discuss your experience with models like semantic embeddings and personalisation algorithms, as these will be key in the role.
✨Showcase Your Leadership Skills
Since this position involves managing a team of ML Scientists, prepare examples that highlight your leadership style. Think about how you've fostered collaboration and continuous learning in your previous roles, and be ready to share specific instances.
✨Prepare for Technical Questions
Expect some technical questions related to Python, Spark, and frameworks like PyTorch or TensorFlow. Brush up on your hands-on experience with these tools, and be prepared to discuss how you've used them in past projects.
✨Communicate Clearly
You’ll need to bridge complex ML concepts for diverse audiences, so practice explaining your work in simple terms. Think about how you can convey your insights and best practices in a way that’s accessible to both technical and non-technical stakeholders.
