Machine Learning Engineer, Platform

Навыки

• AI-инструменты в работе
• Коммуникация
• LLM
• Machine Learning
• Python
• RAG

О компании и продукте

• At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.
• We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
• We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.
• We comply with the United States Department of Labor's Pay Transparency provision .

Задачи

• Own large areas of platform end to end, driving components from design through to production deployment
• Work on knowledge representation systems, including ontologies and knowledge graphs, to support structured reasoning over enterprise data
• Design and implement RAG pipelines, including chunking, embedding, indexing, retrieval, and reranking
• Build and maintain integrations between retrieval and ML components and diverse enterprise data sources, vector databases, APIs, and services
• Develop context retrieval systems that balance recall, precision, latency, and cost
• Build evaluation frameworks, datasets, and metrics to measure retrieval quality, context relevance, and end to end agent performance
• Build reliable backend services and data pipelines that support ML and LLM components in production
• Deliver experiments and new capabilities quickly, maintaining high quality and tight feedback loops with customers
• Collaborate across product, ML, and infrastructure teams to shape the direction of the platform
• London, UK
• Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform that provides APIs for knowledge retrieval, inference, evaluation, and agentic workflows
• You will own ML components end to end — from research and prototyping through to production deployment — working across knowledge bases, vector stores, RAG pipelines, and context engines to power agents that deliver real impact for enterprise customers

Требования

• 5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases
• Strong engineering fundamentals, supported by a Master’s or PhD degree in Computer Science, Machine Learning, AI, or equivalent practical experience
• A deep, hands-on understanding of retrieval systems, RAG, embeddings, vector indexing, and knowledge representation
• Proven proficiency in Python, including writing production-quality, testable, and maintainable code

Будет плюсом

• Experience with knowledge representation, semantic search, or agentic systems
• Experience scaling or shipping products at high-growth startups
• The ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints
• Strong communication skills and comfort working in customer-facing or cross-functional environments
• PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role
• This allows us to ensure a fair and thorough evaluation of all applicants

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