Senior Machine Learning Scientist

Company Description

Depop is a peer-to-peer circular fashion marketplace where anyone can buy, sell and discover secondhand fashion. Our mission is simple: to make fashion circular by making secondhand as exciting and rewarding as buying new.

Founded in 2011, Depop’s diverse community has helped move resale into the mainstream, where buying secondhand is no longer an alternative, but how people of different ages now engage with fashion. Today, more than 56 million registered users come to Depop to find great value, express their own personal style and give clothes a longer life. We believe that everything you want already exists, and our role is to help people discover it.

Powered by a team of over 500 people, our company is headquartered in London, with offices in New York. In 2021, Depop became a wholly-owned subsidiary of Etsy - the global marketplace for unique and creative goods - and continues to operate as a standalone company. For more information, visit www.depop.com

We aim 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.

AI Disclosure: We use AI tools (Google Gemini) to help our team source and review applications for roles with a high volume of applications. These tools assist our recruiters in identifying great talent but do not replace human decision-making. At Depop, every hiring decision is made by a human.

If, due to a disability, you need adjustments to complete the application, please let us know by sending an email with your name, the role to which you would like to apply, and the type of support you need to complete the application to [email protected].

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 own the design, development, and evolution of machine learning systems that detect and prevent harmful or policy-violating content across the platform.

You will work on high-impact trust, safety, and fraud problems such as phishing prevention, counterfeit detection, and identifying prohibited or restricted listings. This role requires operating in ambiguous and adversarial environments, where you will define problems, shape solutions, and deliver robust systems that scale. Your work will leverage modern deep learning and large language models to drive meaningful improvements in user safety and platform integrity.

Responsibilities:

• Own end-to-end machine learning solutions, from problem framing and data strategy through to modelling, deployment, and iteration in production

• Design and build scalable ML systems to detect fraud, abuse, and policy violations in user-generated content across text and multimodal domains

• Lead the development and application of LLM-based approaches, including model selection, fine-tuning, evaluation, and failure analysis

• Define and drive experimentation strategy, including offline evaluation and online testing, to rigorously measure impact and inform product decisions

• Work in ambiguous, evolving problem spaces, proactively identifying new risks and shaping detection strategies in partnership with Trust, Policy, and Product

• Collaborate cross-functionally to translate business and safety goals into effective, production-ready ML systems, influencing trade-offs and priorities

• Communicate clearly and effectively with both technical and non-technical stakeholders, articulating approaches, trade-offs, and impact

Qualifications:

• Proven track record of designing, deploying, and iterating on machine learning systems that deliver measurable impact in production environments

• Strong foundation in machine learning and deep learning, with hands-on experience using frameworks such as PyTorch and modern architectures (e.g. Transformers, large language models)

• Experience applying ML inreal-world, noisy, and adversarial domains, such as trust & safety, fraud, or abuse detection

• Proficiency in Python and experience writing production-quality code, with a solid understanding of data pipelines, model training workflows, and MLOps practices

• Demonstrated ability to own problems end-to-end, operate in ambiguous environments, and make pragmatic technical decisions

• Strong collaboration and communication skills, with the ability to influence cross-functional partners and stakeholders

Bonus points:

• Experience building ML systems for trust, safety, fraud, or policy enforcement use cases

• Hands-on experience fine-tuning, evaluating, or deploying large language models in production settings

• Experience with multimodal modelling (e.g. text + image)

• Familiarity with human-in-the-loop systems or moderation workflows

• Experience with Databricks, PySpark, or large-scale data processing systems

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 of annual leave with the option to carry over up to 5 days
• Impact hours: Up to 2 days of 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

Family Life:

• For birth parent: 20 weeks of paid parental leave for full-time regular employees
• For non-birth parents: 12 weeks of paid parental leave for full-time regular employees
• IVF leave, shared parental leave, and paid emergency parent/carer leave

Learn + Grow:

• Twice-yearly development chats and yearly performance reviews
• Learning budget
• Upskilling our employees with company-wide training workshops, materials and resources

Your Future:

• Life Insurance (financial compensation of 3x your salary)
• Pension matching up to 6% of full base salary with Aviva

Depop Extras:

• In-office Depop Shop (that’s free!) and a packing station with free delivery.
• Special milestones are celebrated with gifts and rewards!