Drift AI
Machine Learning Engineer, Platform

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Machine Learning Engineer
London, UK
About The Role
Applied Intelligence Systems (AIS) is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. AIS spans multiple workstreams — agent evaluation and oversight, orchestration and tool-use infrastructure, model and systems optimization, and applied research on new agent capabilities. This role owns the context and memory capabilities within AIS, including their correctness, performance, and evaluation.
We are looking for a Machine Learning Engineer who can own hard technical problems 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.
What You'll Do
- Own large areas of the platform end to end, 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.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
What We Look For
- 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, knowledge representation, and semantic search.
- Proven proficiency in Python, including writing production-quality, testable, and maintainable code.
- 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.
- Experience scaling products at hyper growth startups
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.
About Us
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.


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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.
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