At a Glance
- Tasks: Join our Core ML team to build and maintain cutting-edge machine learning models for fashion resale.
- Company: Depop is a vibrant platform revolutionising the fashion resale market with innovative technology.
- Benefits: Enjoy flexible working options, a collaborative culture, and opportunities for professional growth.
- Other info: Bonus points for experience with NLP, deep learning, and cloud platforms like AWS.
- Why this job: Make a real impact in the fashion industry while working with a passionate, cross-functional team.
- Qualifications: Significant experience in machine learning, strong Python skills, and a knack for leading projects.
The predicted salary is between 54000 - 84000 £ per year.
Role
At Depop, machine learning is integral to building a safe and trusted marketplace. As a Senior Machine Learning Scientist in the Trust Detection team, you will design and build state‑of‑the‑art machine learning systems to detect and prevent harmful or policy‑violating content across the platform. You'll work on trust, safety, and fraud problems such as phishing prevention, counterfeit detection, and identifying prohibited or restricted listings (e.g. regulated or restricted item categories). The solutions you build will primarily leverage large language models and deep learning techniques to operate at scale and with high performance.
Responsibilities
- Research, design, and deliver machine learning solutions to detect fraud, abuse, and policy violations in user‑generated content
- Work closely with Trust, Product, Policy, and Engineering partners to translate business and safety requirements into effective ML systems
- Build, train, and evaluate LLM‑based models for text and multimodal classification, detection, and reasoning tasks
- Set up and run large‑scale offline experiments and online evaluations to test hypotheses and measure impact
- Stay up to date with state‑of‑the‑art research in large language models and modern deep learning, applying new techniques where appropriate
- Participate in team ceremonies including agile rituals, technical design discussions, and roadmap planning
- Clearly communicate technical approaches, results, and trade‑offs to both technical and non‑technical stakeholders
Qualifications
- Experience working as a Machine Learning Scientist, with a track record of delivering models to solve real‑world, production‑scale problems
- Strong understanding of machine learning fundamentals, with hands‑on experience using frameworks such as PyTorch and modern architectures (e.g. Transformers, large language models)
- Proficiency in Python, with the ability to write production‑quality code and a solid understanding of data pipelines, model training, and MLOps practices
- Comfortable working with noisy, weakly‑labeled, or imbalanced data typical of trust and safety domains
- Collaborative, pragmatic, and curious team player, able to work effectively with cross‑functional partners
- Passion for learning, experimentation, and staying current with advances in machine learning
Bonus points
- Experience building classification or scoring models for trust, safety, fraud, abuse, or policy enforcement use cases
- Hands‑on experience fine‑tuning, evaluating, or deploying large language models for real‑world applications
- Experience with experiment design, offline evaluation, and online testing (e.g. A/B tests)
- Experience working with Databricks and PySpark
- Experience deploying ML systems on AWS or other cloud platforms (GCP/Azure)
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!
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Senior Machine Learning Scientist in London employer: Depop
DEPOP is an excellent employer that fosters a collaborative and innovative work culture, where Senior iOS Engineers can thrive while leading architecture for impactful user experiences. Located in Greater London, employees enjoy flexible working options, comprehensive healthcare access, and generous leave policies, all of which support a healthy work-life balance. With opportunities for mentorship and professional growth, DEPOP is dedicated to empowering its team members to make meaningful contributions to the secondhand fashion marketplace.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Scientist in London
✨Tip Number 1
Familiarise yourself with the latest trends and advancements in machine learning, particularly in areas like computer vision and NLP. This will not only help you during interviews but also demonstrate your passion and commitment to the field.
✨Tip Number 2
Engage with the machine learning community by attending meetups, webinars, or conferences. Networking with professionals in the industry can provide valuable insights and potentially lead to referrals for the position.
✨Tip Number 3
Prepare to discuss your previous projects in detail, especially those that involved scaling models and working with cross-functional teams. Be ready to explain your thought process and the impact of your work on real-world problems.
✨Tip Number 4
Practice communicating complex technical concepts in a clear and concise manner. Since the role requires interaction with both technical and non-technical stakeholders, honing this skill will be crucial for your success in the interview.
We think you need these skills to ace Senior Machine Learning Scientist in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights relevant experience in machine learning, particularly with frameworks like Transformers, PyTorch, or TensorFlow. Emphasise any projects where you've led technical direction or mentored others.
Craft a Compelling Cover Letter:In your cover letter, express your passion for machine learning and the fashion resale space. Discuss specific projects that demonstrate your ability to deliver robust solutions and how you can contribute to Depop's goals.
Showcase Your Technical Skills:Include examples of your work with Python, data engineering, and MLOps principles. If you have experience with NLP, image classifiers, or cloud platforms like AWS, make sure to mention these as they are bonus points for the role.
Prepare for Interviews:Be ready to discuss your past projects in detail, especially those involving large-scale experiments and model productionisation. Practice explaining complex technical concepts in a way that non-technical stakeholders can understand.
How to prepare for a job interview at Depop
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with machine learning frameworks like Transformers, PyTorch, or TensorFlow. Highlight specific projects where you've successfully delivered and scaled models, and be ready to explain the technical challenges you faced and how you overcame them.
✨Demonstrate Leadership Skills
As a senior role, it's crucial to show that you can lead end-to-end ML projects. Share examples of how you've taken ownership of high-impact projects, mentored junior team members, and collaborated with cross-functional teams to achieve common goals.
✨Communicate Clearly
Practice explaining complex technical concepts in simple terms. You'll need to communicate findings to both technical and non-technical audiences, so being able to articulate your ideas clearly will set you apart from other candidates.
✨Prepare for Problem-Solving Questions
Expect to tackle hypothetical scenarios or case studies related to machine learning in the fashion resale space. Think about how you would approach designing general-purpose ML solutions and conducting experiments, ensuring you can demonstrate statistical rigour and real-world applicability.