Scale AI
Machine Learning Engineer, Platform
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Machine Learning Engineer, Platform
Machine Learning Engineer (Retrieval & Knowledge Systems)
London, UK
About Scale GP
Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform that delivers APIs for:
- Knowledge retrieval
- Inference
- Evaluation
- Agentic workflows
This role focuses on building and refining the ML-driven knowledge systems at the core of the platform. You’ll own end-to-end ML components, from prototyping to production deployment, driving systems that:
- Power structured reasoning over enterprise data
- Enable RAG (Retrieval-Augmented Generation) pipelines
- Optimise context engines for agents
The platform interacts with a wide range of enterprise data sources, vector databases, and APIs to deliver actionable AI outcomes for customers.
Key Responsibilities
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End-to-End System Ownership
- Design, build, and deploy large-scale ML retrieval systems from research to production.
- Ensure balancing trade-offs between recall, precision, latency, and cost.
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Knowledge Representation
- Develop ontologies and knowledge graphs for structured data reasoning.
- Contribute to semantic search architectures and agentic workflows.
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RAG Pipeline Engineering
- Implement chunking, embddings, indexing, retrieval, and reranking pipelines.
- Optimise for high-quality context in NLG tasks.
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Integration & Scalability
- Build robust integrations with enterprise data sources, vector databases, and APIs.
- Ensure scalable, observable, and high-performance backend services.
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Evaluation & Measurement
- Design metric-driven evaluation frameworks for retrieval quality, context relevance, and agentic performance.
- Curate datasets and benchmarks for continuous improvement.
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Production Deployment
- Deliver low-latency, reliable ML systems in production environments.
- Establish metric-driven feedback loops between engineering and customer-facing teams.
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Collaboration & Shaping Platform Direction
- Work closely with product, ML, and infrastructure teams to define technical roadmaps.
- Drive research-and-product alignment in ambiguous, high-impact scenarios.
Requirements & Expectations
Essential
- 5+ years of experience building ML/AI systems for production infrastructure.
- Master’s or PhD in Computer Science, Machine Learning, AI, or strong equivalent practical experience.
- Deep expertise in retrieval systems, RAG, embeddings, vector stores, and knowledge graphs.
- Hands-on proficiency in Python (including clean, tested, modular code).
- Experience shipping technology at a high-growth startup environment.
- Strong problem-solving aptitude, bridging research needs and practical constraints.
- Ability to communicate clearly with technical and non-technical stakeholders.
Preferred
- Experience in knowledge graphs, semantic search, or agentic systems.
- Knowledge of enterprise AI, including ragioning for domain-specific data.
- Familiarity with vector databases (e.g. Milvus, Pinecone), LLM orchestration, or inference pipelines.
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About Scale
Mission: Build the reliable tech stack enabling critical AI decision-making for enterprises and organisations worldwide.
Our clients include leading firms in tech, healthcare, finance, and public sector, such as:
- Meta
- Ernst & Young
- Mayo Clinic
- U.S. DoD (Army, Air Force)
- Government of Qatar
As an inclusive, diverse, and equal opportunity employer, we are committed to fostering an environment where everyone feels valued and can contribute their best work.
Privacy note: We treat application data with respect, adhering to internal policies aligned with GDPR and evolving compliance standards. Candidates will not be charged for application/recruitment services.
PLEASE NOTE: Due to portfolio management, we maintain a mandatory 90-day gap before reconsidering applications for the same role.
Apply today to shape the future of enterprise AI at Scale.
Additional Policies
- Reasonable accommodations: Contact accommodations@scale.com if you need support during the application process.
- Pay transparency: Scale complies with US DOL pay transparency regulations.
- Data usage: Personal details are used for job matching and internal HR purposes only, per our privacy policy.
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