Staff Software Engineer - Machine Learning
Capital One · EC1Y8AF · posted 2 days ago
Going rate £54,700UK median £56,914
Home Office going rates from
Occupation
2134Programmers and software development professionals
Going rate for this occupation: £54,700 · UK median pay £56,914
Home Office going rates from
Where this salary sits
- UK pay for this occupation
- This role£55,947 to £100,274estimated
- Going rate£54,700
- UK median£56,914
View these figures as a table
| Percentile | Pay |
|---|---|
| 10th | £32,835 |
| 25th | £42,289 |
| 50th | £56,914 |
| 75th | £75,794 |
| 90th | £102,860 |
| Going rate | £54,700 |
| UK median | £56,914 |
Sponsorship
Sponsorship chance
Low
- Not matched to a licence on the register
- Occupation is eligible for Skilled Worker
- Estimated salary clears the going rate
On the public records we hold, sponsorship for this role looks unlikely: not matched to a licence on the register, and occupation is eligible for Skilled Worker.
- The salary is an estimate from national earnings data, not the employer's figure.
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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
Information from public records, not immigration advice.