Senior Machine Learning Engineer, Recommendations
Senior Machine Learning Engineer, Recommendations

Senior Machine Learning Engineer, Recommendations

Full-Time 48000 - 84000 £ / year (est.) No home office possible
Depop

At a Glance

  • Tasks: Join the Recommendations team to build and optimise machine learning systems for personalised product recommendations.
  • Company: Depop, a vibrant tech company focused on innovation and collaboration.
  • Benefits: Health benefits, flexible working, generous leave, and professional development opportunities.
  • Why this job: Make a real impact with cutting-edge ML technology in a dynamic environment.
  • Qualifications: Experience in ML pipelines and strong communication skills are essential.
  • Other info: Enjoy a dog-friendly office and celebrate milestones with exciting rewards!

The predicted salary is between 48000 - 84000 £ per year.

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:
  • Reproducible training workflows
  • CI/CD for model deployment
  • Real-time and batch model serving
  • Online/offline feature consistency
  • Monitoring and alerting
  • 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 dependent). 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 employer: Depop

    Depop is an exceptional employer that fosters a collaborative and innovative work culture, particularly within the Recommendations team in the UK. With a strong emphasis on employee well-being, flexible working arrangements, and ample opportunities for professional growth, Depop ensures that its team members are supported both personally and professionally. The company also celebrates milestones and encourages a healthy work-life balance, making it an attractive place for those seeking meaningful and rewarding employment.
    Depop

    Contact Detail:

    Depop Recruiting Team

    StudySmarter Expert Advice 🤫

    We think this is how you could land Senior Machine Learning Engineer, Recommendations

    ✨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 job descriptions just can't.

    ✨Tip Number 2

    Show off your skills! Create a portfolio showcasing your ML projects, especially those related to recommendations. This is your chance to shine and demonstrate what you can bring to the table.

    ✨Tip Number 3

    Prepare for the interview by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts simply, as you'll need to communicate 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 joining the team at Depop.

    We think you need these skills to ace Senior Machine Learning Engineer, Recommendations

    Machine Learning Pipelines
    Recommendation Systems
    Retrieval Models
    Embedding Generation
    Cloud Environments (AWS, GCP)
    Python
    ML Frameworks (PyTorch, TensorFlow, scikit-learn)
    MLOps Tooling (SageMaker, MLflow, TFServing)
    CI/CD Best Practices
    Streaming and Batch Systems (Kafka, Airflow, RabbitMQ)
    Communication Skills
    Problem-Solving Skills
    Operational Excellence
    Monitoring and Alerting

    Some tips for your application 🫡

    Tailor Your CV: Make sure your CV is tailored to the Senior Machine Learning Engineer role. Highlight your experience with ML pipelines, recommendation systems, and any relevant technologies like Python and AWS. We want to see how your skills match what we're looking for!

    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 Recommendations team. Be sure to mention any specific projects or experiences that relate to the job description.

    Show Off Your Projects: If you've worked on any cool ML projects, don’t hesitate to showcase them! Whether it's through GitHub links or detailed descriptions in your application, we love seeing practical examples of your work and how you’ve tackled challenges in the past.

    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 you need about the role and our company culture there!

    How to prepare for a job interview at Depop

    ✨Know Your ML Pipelines

    Make sure you can talk confidently about your experience with building and deploying ML pipelines. Be ready to discuss specific projects where you've designed, implemented, or optimised these systems, especially in relation to recommendation models.

    ✨Brush Up on Your Tech Stack

    Familiarise yourself with the technologies mentioned in the job description, like Python, PyTorch, and AWS services. Be prepared to explain how you've used these tools in past roles, particularly in the context of real-time and batch model serving.

    ✨Communicate Clearly

    Since you'll be working with both technical and non-technical stakeholders, practice explaining complex concepts in simple terms. Think of examples where you've successfully communicated your ideas or collaborated with cross-functional teams.

    ✨Show Your Ownership Mindset

    Demonstrate your ability to take ownership of projects. Share examples of times when you've worked independently to solve problems or improve processes, and highlight how this mindset aligns with the company's engineering culture.

    Senior Machine Learning Engineer, Recommendations
    Depop

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