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Capital One

Staff Software Engineer - Machine Learning

Capital One is hiring a Staff Software Engineer - Machine Learning in London, United Kingdom. Level rates it ; you can apply on Capital One.

Level's read on this job

  • On Level's scale, this software engineer role is Builds AI ●●●●: the job is building AI systems.
  • Tools and skills the posting cites include LangChain, LangGraph and PyTorch.
  • Level classifies it as a senior role.
  • Level lists 2,000+ other open roles at Capital One.

AI in this role

Lead machine learning and AI technical strategy, driving scalable ML systems, MLOps, and GenAI solutions.

LangChainLangGraphPyTorchTensorFlowscikit-learnPythonAWSAzureGoogle Cloud
ragml-opsai-safetymachine-learningmlopssystem-designgenerative-aicloud-native
White Collar Factory (95009), United Kingdom, London, London

Staff Software Engineer - Machine Learning

About this role

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

Do you love shaping the technical landscape and driving innovation across the organisation?

Are you passionate about solving complex ML and AI challenges and supporting multiple teams toward a shared technical vision?

At Capital One, 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.

What You'll Do

  • 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

What we're looking for

  • 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

Where and how you'll work

This is a permanent position based in our London office.

We have a hybrid working model which gives you flexibility to work from our office and from home.

We're big on collaboration and connection, so you'll be based in our London office 3 days a week on Tuesdays, Wednesdays and Thursdays.

What's in it for you

  • Bring us all this - and you'll be well rewarded with a role contributing to the roadmap of an organisation committed to transformation
  • We offer high performers strong and diverse career progression, investing heavily in developing great people through our Capital One University training programmes (and appropriate external providers)
  • Immediate access to our core benefits including pension scheme, bonus, generous holiday entitlement and private medical insurance - with flexible benefits available including season-ticket loans, cycle to work scheme and enhanced parental leave
  • Open-plan workspaces and accessible facilities designed to inspire and support you. Our Nottingham head-office has a fully-serviced gym, subsidised restaurant, mindfulness and music rooms.

What you should know about how we recruit

We pride ourselves on hiring the best people, not the same people. Building diverse and inclusive teams is the right thing to do and the smart thing to do. We want to work with top talent: whoever you are, whatever you look like, wherever you come from. We know it's about what you do, not just what you say. That's why we make our recruitment process fair and accessible. And we offer benefits that attract people at all ages and stages.

We also partner with organisations including the Women in Finance and Race At Work Charters, Stonewall and upReach to find people from every walk of life and help them thrive with us. We have a whole host of internal networks and support groups you could be involved in, to name a few:

  • REACH - Race Equality and Culture Heritage group focuses on representation, retention and engagement for associates from minority ethnic groups and allies
  • OutFront - to provide LGBTQ+ support for all associates
  • Mind Your Mind - signposting support and promoting positive mental wellbeing for all
  • Women in Tech - promoting an inclusive environment in tech
  • EmpowHER - network of female associates and allies focusing on developing future leaders, particularly for female talent in our industry
  • Enabled - focused on supporting associates with disabilities and neurodiversity

Capital One is committed to diversity in the workplace.

If you require a reasonable adjustment, please contact ukrecruitment@capitalone.com All information will be kept confidential and will only be used for the purpose of applying a reasonable adjustment.

For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com

Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.

Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

How we rate this

Staff Software Engineer - Machine Learning at Capital One rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGML OpsAI SafetyMachine learningMLOpsSystem DesignGenerative AICloud Native

Also common in software engineer jobs at this level

Pay

Typical software engineer job
$167k

This posting doesn't list pay. Use the typical software engineer figure when pay comes up. See software engineer jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you monitor a model once it's live, and how do you know it needs retraining?
  3. How do you think about the risk of an AI system in this kind of role failing silently?
  4. Tell me about a project where machine learning was part of your work. What did you do?
  5. Tell me about a project where mlops was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: RAG, ML Ops, AI Safety, Machine learning, and MLOps. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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