# Staff Software Engineer - Machine Learning

**Company**: Capital One
**Location**: London
**Work arrangement**: hybrid
**Experience**: staff
**Job type**: full-time
**Category**: Engineering
**Industry**: Finance

**Apply**: https://capitalone.wd12.myworkdayjobs.com/en-US/Capital_One/job/London-Eng/Staff-Software-Engineer---Machine-Learning_R242460-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_30cdbbc9-48f

## Description

We're on a mission to transform the way we use data and AI to service our customers and drive efficiency across the business.

As a Staff Software Engineer - Machine Learning, you'll be part of a community of technical leaders who drive engineering excellence, foster innovation, and deliver impactful ML/AI and Gen AI solutions that meet real customer needs.

**Responsibilities**

- Own and drive the ML/AI technical strategy for UK use cases, spanning multiple teams and influencing the overall technical direction for AI adoption

- Lead and coordinate ML engineering efforts across multiple teams, ensuring alignment with broader business objectives, enterprise platform capabilities, and technology strategy

- Provide technical consultancy to teams delivering AI use cases, guiding architectural decisions, solution design, and effective use of enterprise ML/AI platforms and capabilities

- Proactively identify emerging ML/AI patterns, define and evangelise best practices, and establish reusable approaches that enhance delivery of AI use cases across the business

- Drive MLOps standards and practices across teams, including CI/CD for models, automated testing, monitoring, and deployment pipelines

- Collaborate with enterprise platform and data science teams, contributing to platform capabilities where appropriate and partnering on use case delivery

- Build and maintain strong relationships with key stakeholders, including senior leadership, product owners, data science teams, and enterprise platform partners

- Represent Capital One in external ML/AI technical forums, contributing to industry discussions

- Develop and advocate for strategies to proactively manage technical debt across ML/AI systems

- Actively mentor and develop engineers, fostering a culture of continuous learning

**Requirements**

- Deep expertise in Python and ML engineering

- Deep expertise in ML/AI systems design, MLOps, and cloud-native architectures

- Track record of leading ML/AI technical initiatives across multiple teams

- Strong experience with cloud platforms (AWS, Azure, GCP)

- Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG)

- Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management for AI systems

- Experience designing and scaling low-latency, customer-facing ML/AI architectures

- Proven experience setting a multi-team ML/AI technical vision and strategy

- Strong track record of technical leadership and influence without authority

- Experience driving ML engineering standards and best practices across organisations

- Deep understanding of the full ML/AI development lifecycle, including model serving, data pipelines, and Gen AI systems

- Experience leveraging enterprise platforms to deliver business use cases at scale

- Experience of steering Communities of Practice or technical forums

- Strong business acumen and ability to translate ML/AI concepts for various audiences

**Benefits**

- Competitive salary

- High performers strong and diverse career progression

- Capital One University training programmes

- Pension scheme, bonus, generous holiday entitlement, and private medical insurance

- Flexible benefits, including season-ticket loans, cycle to work scheme, and enhanced parental leave

**Work Environment**

- Permanent position based in London office

- Hybrid working model, with 3 days a week in the office

## Skills

### Required
- Python
- ML engineering
- ML/AI systems design
- MLOps
- cloud-native architectures
- cloud platforms (AWS, Azure, GCP)
- ML frameworks (PyTorch, TensorFlow, scikit-learn)
- Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG)
- responsible AI practices

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Source: [Apply at capitalone.wd12.myworkdayjobs.com](https://capitalone.wd12.myworkdayjobs.com/en-US/Capital_One/job/London-Eng/Staff-Software-Engineer---Machine-Learning_R242460-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)