Senior Machine Learning Scientist

Depop

Senior Machine Learning Scientist in Trust Detection team at Depop designing ML systems to detect harmful content. Leveraging large language models for safety, fraud prevention, and policy adherence.

Posted 4/18/2026full-timeLondon • 🇬🇧 United KingdomSeniorWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPySparkPythonPyTorch

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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)

Benefits

Comp & perks

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
25 days annual leave with option to carry over up to 5 days
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.
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
18 weeks of paid parental leave for full-time regular employees
IVF leave, shared parental leave, and paid emergency parent/carer leave
Budgets for conferences, learning subscriptions, and more
Mentorship and programmes to upskill employees
Life Insurance (financial compensation of 3x your salary)
Pension matching up to 6% of qualifying earnings
Employees enjoy free shipping on their Depop sales within the UK. Special milestones are celebrated with gifts and rewards!

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Hard Skills & Tools

machine learninglarge language modelsdeep learningPyTorchPythonMLOpsdata pipelinesclassification modelsexperiment designA/B testing

Soft Skills

collaborativepragmaticcuriousteam playercommunication