At a Glance
- Tasks: Join the Recommendations team to build and optimise machine learning systems for personalised product recommendations.
- Company: Depop, a vibrant community-driven fashion marketplace with over 35 million users.
- Benefits: Enjoy flexible working, generous leave, health support, and opportunities for professional growth.
- Other info: Inclusive workplace culture that values diversity and innovation.
- Why this job: Make a real impact in circular fashion while working with cutting-edge machine learning technologies.
- Qualifications: Experience in ML pipelines and strong communication skills are essential.
The predicted salary is between 60000 - 80000 £ per year.
Depop is the community-powered circular fashion marketplace where anyone can buy, sell and discover desirable secondhand fashion. With a community of over 35 million users, Depop is on a mission to make fashion circular, redefining fashion consumption. Founded in 2011, the company is headquartered in London, with offices in New York and Manchester, and in 2021 became a wholly-owned subsidiary of Etsy.
Our mission is to make fashion circular and to create an inclusive environment where everyone is welcome, no matter who they are or where they’re from. Just as our platform connects people globally, we believe our workplace should reflect the diversity of the communities we serve. We thrive on the power of different perspectives and experiences, knowing they drive innovation and bring us closer to our users. We’re proud to be an equal opportunity employer, providing employment opportunities without regard to age, ethnicity, religion or belief, gender identity, sex, sexual orientation, disability, pregnancy or maternity, marriage and civil partnership, or any other protected status. We’re continuously evolving our recruitment processes to ensure fairness and are open to accommodating any needs you might have.
Depop is looking for a Machine Learning Engineer to join the Recommendations team in the UK. You will work alongside ML Scientists, Backend Engineers, MLOps and other ML Engineers to build, deploy, maintain, and monitor the machine learning systems that power personalised product recommendations across key surfaces across the app.
Responsibilities
- Design and implement pipelines for training, evaluating, deploying, and monitoring retrieval models
- Work closely with ML Scientists to productionise recommendation models, improving reliability, latency, and observability
- Build and optimise embedding generation and recommendations serving
- Partner with backend and product teams to define integration requirements and coordinate deployments of recommendation services
- Help extend the recommendations ML infrastructure in collaboration with MLOps, including:
- Maintain high standards for operational excellence, testing, and incident response
- Contribute to a strong engineering culture focused on scalability, experimentation, and measurable impact
Required Skills and Experience
- Proven experience building and deploying ML pipelines in production
- Experience with recommendation, retrieval, or ranking systems (e.g. two-tower models, embeddings, candidate generation)
- Solid understanding of ML workflows from research to production
- Strong ownership mindset and ability to work independently
- Excellent communication skills across technical and non-technical stakeholders
- Experience designing systems in modern cloud environments (e.g. AWS, GCP)
Technologies and Tools
- Python
- ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn)
- ML/MLOps tooling (e.g. SageMaker, MLflow, TFServing)
- Spark and Databricks
- AWS services (e.g. IAM, S3, Redis, ECS)
- CI/CD tooling and best practices
- Streaming and batch systems (e.g. Kafka, Airflow, RabbitMQ)
Additional Information
Health + Mental Wellbeing
- PMI and cash plan healthcare access with Bupa
- Subsidised counselling and coaching with Self Space
- Cycle to Work scheme with options from Evans or the Green Commute Initiative
- Employee Assistance Programme (EAP) for 24/7 confidential support
- Mental Health First Aiders across the business for support and signposting
Work/Life Balance
- 25 days annual leave with option to carry over up to 5 days
- 1 company-wide day off per quarter
- Impact hours: Up to 2 days additional paid leave per year for volunteering
- Fully paid 4 week sabbatical after completion of 5 years of consecutive service with Depop, to give you a chance to recharge or do something you love.
Flexible Working
- MyMode hybrid-working model with Flex, Office Based, and Remote options *role dependant
- All offices are dog-friendly
- Ability to work abroad for 4 weeks per year in UK tax treaty countries
Family Life
- 18 weeks of paid parental leave for full-time regular employees
- IVF leave, shared parental leave, and paid emergency parent/carer leave
Learn + Grow
- Budgets for conferences, learning subscriptions, and more
- Mentorship and programmes to upskill employees
Your Future
- Life Insurance (financial compensation of 3x your salary)
- Pension matching up to 6% of qualifying earnings
Depop Extras
- Employees enjoy free shipping on their Depop sales within the UK.
- Special milestones are celebrated with gifts and rewards!
Senior Machine Learning Engineer, Recommendations in London employer: Dormont Manufacturing Co
Depop is an exceptional employer that champions a diverse and inclusive work environment, reflecting the vibrant community it serves. With a strong focus on employee well-being, offering generous benefits such as 25 days of annual leave, mental health support, and flexible working options, Depop fosters a culture of growth and innovation. Employees are encouraged to develop their skills through mentorship and learning opportunities, making it a rewarding place for those looking to make a meaningful impact in the circular fashion industry.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Engineer, Recommendations in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Depop. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! If you've got a portfolio or GitHub with projects related to machine learning, make sure to highlight them. Real-world examples of your work can set you apart from the crowd.
✨Tip Number 3
Prepare for the interview by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts in simple terms – it shows you can communicate effectively with both techies and non-techies.
✨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 being part of the Depop community.
We think you need these skills to ace Senior Machine Learning Engineer, Recommendations in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of Senior Machine Learning Engineer. Highlight your experience with ML pipelines, recommendation systems, and any relevant technologies like Python and AWS. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for circular fashion and how your background in machine learning can contribute to our mission at Depop. Keep it engaging and personal – we love to see your personality come through!
Showcase Your Projects:If you've worked on any cool projects related to ML or recommendations, don’t hold back! Include links to your GitHub or any relevant portfolios. We’re keen to see your hands-on experience and how you tackle real-world problems.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, you’ll find all the details about the role and our company culture there!
How to prepare for a job interview at Dormont Manufacturing Co
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts, especially around recommendation systems. Be ready to discuss your experience with building and deploying ML pipelines, as well as any specific models you've worked with, like two-tower models or embeddings.
✨Show Off Your Collaboration Skills
Depop values teamwork, so be prepared to talk about how you've worked with cross-functional teams in the past. Highlight any experiences where you've partnered with ML scientists or backend engineers to bring a project to life.
✨Demonstrate Your Problem-Solving Mindset
Think of examples where you've tackled challenges in ML workflows or improved system reliability. Show them that you can think critically and adapt to changing requirements, which is key for this role.
✨Get Familiar with Their Tech Stack
Before the interview, take some time to learn about the tools and technologies mentioned in the job description, like AWS, PyTorch, and CI/CD practices. Being able to speak confidently about these will show you're serious about the role and ready to hit the ground running.
