At a Glance
- Tasks: Lead ML/AI technical strategy and drive innovative projects across multiple teams.
- Company: Join Capital One, a leading tech company transforming banking with ingenuity and humanity.
- Benefits: Enjoy competitive salary, generous holiday, private medical insurance, and flexible benefits.
- Other info: Hybrid work model with strong career progression and a culture of continuous learning.
- Why this job: Make a real impact in AI while collaborating with diverse teams and driving innovation.
- Qualifications: Expertise in Python, ML engineering, and cloud platforms like AWS and Azure required.
The predicted salary is between 62000 - 102000 £ per year.
Salary: £62,000 - 102,000 per year
Requirements:
- Deep expertise in Python and ML engineering.
- Deep expertise in ML/AI systems design, MLOps, and cloud-native architectures.
- Strong track record of leading ML/AI technical initiatives across multiple teams.
- Experience with cloud platforms such as AWS, Azure, and GCP.
- Experience with ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
- Experience with Gen AI and agentic frameworks such as LangGraph, LangChain, VectorDBs, and RAG.
- Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management.
- Experience designing and scaling low-latency, customer-facing ML/AI architectures.
- Proven experience setting multi-team ML/AI technical vision and strategy.
- Strong 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 steering Communities of Practice or technical forums.
- Strong business acumen and the ability to translate ML/AI concepts for different audiences.
Responsibilities:
- Own and drive the ML/AI technical strategy for UK use cases across multiple teams.
- Lead and coordinate ML engineering efforts, ensuring alignment with business objectives, enterprise platform capabilities, and technology strategy.
- Provide technical consultancy to teams delivering AI use cases, guiding architectural decisions and solution design.
- Define and evangelise best practices and reusable approaches that improve delivery of AI use cases across the business.
- Drive MLOps standards and practices, including CI/CD for models, automated testing, monitoring, and deployment pipelines.
- Collaborate with enterprise platform and data science teams, contributing to platform capabilities and partnering on use case delivery.
- Build and maintain strong relationships with senior leadership, product owners, data science teams, and enterprise platform partners.
- Represent the company in external ML/AI technical forums and industry discussions.
- Develop strategies to proactively manage technical debt across ML/AI systems.
- Mentor and develop engineers, fostering a culture of continuous learning.
Technologies:
- AI
- AWS
- Azure
- CI/CD
- Cloud
- GCP
- Support
- Machine Learning
- PyTorch
- Python
- TensorFlow
- Network
We are Capital One, a leading information-based technology company on a mission to help our customers succeed by bringing ingenuity, simplicity, and humanity to banking. We are hiring a Staff Software Engineer - Machine Learning for our London office on a permanent hybrid basis, with three days a week onsite in White Collar Factory. We offer strong career progression, investment in learning through our internal university and external providers, and immediate access to core benefits including pension, bonus, generous holiday, private medical insurance, and flexible benefits such as season-ticket loans, cycle to work, and enhanced parental leave. We value collaboration, openness, diversity, and inclusion, and we support a range of internal networks and employee groups that help our people thrive.
Staff Software Engineer - Machine Learning in London employer: Sivara GmbH
At Capital One, we pride ourselves on being an exceptional employer, particularly for our Staff Software Engineer - Machine Learning role based in London. Our commitment to employee growth is evident through strong career progression opportunities and a robust investment in learning, complemented by a vibrant work culture that champions collaboration, diversity, and inclusion. With immediate access to comprehensive benefits and a supportive environment, we empower our employees to thrive both personally and professionally.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Software Engineer - Machine Learning in London
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Sivara GmbH or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Sivara GmbH.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Sivara GmbH.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Sivara GmbH that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Staff Software Engineer - Machine Learning in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Sivara GmbH.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Sivara GmbH and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Sivara GmbH
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Sivara GmbH uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
