Depop
SeniorMachineLearningScientist
“Senior Machine Learning Scientist at Depop. Skills: Machine Learning, Deep Learning, Large Language Models, ML System Design, Production ML. 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”
Industry & Context.
operate in ambiguous and adversarial environments; define problems; shape solutions; deliver robust systems that scale; own problems end-to-end; operate in ambiguous environments; make pragmatic technical decisions
What They're Looking For.
Must Have
designing, deploying, and iterating on machine learning systems that deliver measurable impact in production environments, foundation in machine learning and deep learning, hands-on experience using frameworks such as PyTorch and modern architectures (e.g. Transformers, large language models), Experience applying ML in real-world, noisy, and adversarial domains, such as trust & safety, fraud, or abuse detection, Proficiency in Python, experience writing production-quality code, solid understanding of data pipelines, model training workflows, and MLOps practices, Demonstrated ability to own problems end-to-end, operate in ambiguous environments, make pragmatic technical decisions, collaboration and communication skills, ability to influence cross-functional partners and stakeholders
Nice to Have
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
What You'll Do.
Own end-to-end machine learning solutions
from problem framing and data strategy through to modelling
and iteration in production
Design and build scalable ML systems to detect fraud
and policy violations in user-generated content across text and multimodal domains
Lead the development and application of LLM-based approaches
including model selection
Define and drive experimentation strategy
including offline evaluation and online testing
to rigorously measure impact and inform product decisions
evolving problem spaces
proactively identifying new risks and shaping detection strategies in partnership with Trust
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
How You'll Work.
Team & Collaboration
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; partnership with Trust, Policy, and Product
Communication Scope
Communicate clearly and effectively with both technical and non-technical stakeholders, articulating approaches, trade-offs, and impact; collaboration and communication skills
Process & Methodology
Own end-to-end machine learning solutions, Define and drive experimentation strategy, shape solutions, deliver robust systems that scale
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