At a Glance
- Tasks: Lead a team to develop software for data-driven financial products and integrate machine learning.
- Company: Join a forward-thinking tech company focused on innovation and collaboration.
- Benefits: Competitive salary, performance bonuses, and access to top-notch facilities.
- Other info: Enjoy a hybrid work model and opportunities for personal and professional growth.
- Why this job: Make an impact by integrating AI into real-world applications in finance.
- Qualifications: Experience in leading software teams and knowledge of modern programming languages.
The predicted salary is between 70000 - 90000 € per year.
About this role
We are looking for a Software Engineering Manager who brings a solid foundation in modern development and some experience with Machine Learning environments. You'll lead and grow a team that builds the core software powering our data‐driven financial products, ensuring our models are integrated into seamless, consumer‐facing experiences.
Responsibilities
- Lead & Scale: Support a cross‐functional group of engineers to design, develop, and integrate software features that are vital to the lives of credit card consumers.
- Nurture Talent: Coach and nurture your engineers, including those working on ML integration to achieve their technical, business, and personal goals.
- Bridge the Gap: Collaborate with Product Managers and Data Scientists to ensure ML models are effectively integrated into our production software.
- Build Robust Systems: Oversee the development of platforms that are performant, secure, and capable of handling the unique deployment needs of AI‐powered features.
- Optimize Delivery: Enhance engineering and agile processes, ensuring that model updates and software releases move in sync.
Qualifications
- Leadership Excellence: Proven experience leading and supporting software engineering teams to achieve business goals.
- Technical Breadth: Excellent knowledge of RESTful API development in modern languages (Java, Python, or .Net) and experience with Cloud environments (AWS or Azure).
- AI Awareness: You aren't necessarily a researcher, but you understand how AI fits into the stack, the basics of model inference, data requirements, and how to manage the non‐deterministic nature of AI.
- Strategic Thinking: Comfortable making technical trade‐offs between the need for rapid experimentation and long‐term architectural stability.
- Collaborative Mindset: Ability to communicate effectively across engineering teams to maximize inner‐sourcing and reduce technical debt.
Benefits and Learning Opportunities
- ML Integration at Scale: Take machine learning models out of the lab and into a high‐concurrency production environment.
- Regulated AI: Navigate the complexities of fairness and transparency in a regulated financial landscape.
- Cloud Evolution: Deepen your expertise in AWS/Cloud native tools that support modern intelligent applications.
- Innovation Time: You'll receive 10% of your time to work on cutting‐edge projects such as new AI frameworks or building internal tools.
- Growth: Access to Capital One University and external training to grow as both a leader and a technical strategist.
- Total Reward: Competitive salary, performance bonus, and immediate access to core benefits (pension, private medical, and generous holiday).
- World‐Class Facilities: Nottingham gym, music rooms, London rooftop running track and premium coffee bars.
Location and Working Model
This is a permanent position based in either our London or Nottingham offices. We operate a hybrid working model, with you based in the office three days a week (Tuesdays, Wednesdays, and Thursdays) to foster team connection and collaboration.
Commitment to Diversity and Inclusion
Capital One is committed to diversity in the workplace. We partner with organisations such as Women in Tech and Stonewall to build teams that reflect our customers. Internal networks include REACH (Race Equality and Culture Heritage), OutFront (LGBTQ+ support), and Mind Your Mind.
Legal and Accessibility Statement
If you require a reasonable adjustment, please contact. All information will be kept confidential and will only be used for the purpose of applying a reasonable adjustment.
Engineering Manager - Software & ML in London employer: Richard Fairbank, Capital One
Capital One is an exceptional employer that prioritises employee growth and innovation, particularly in the dynamic fields of software engineering and machine learning. With a commitment to nurturing talent through coaching and access to extensive training resources, employees can thrive in a collaborative environment that values diversity and inclusion. The London and Nottingham offices offer world-class facilities and a hybrid working model, ensuring a balanced work-life experience while engaging in cutting-edge projects that shape the future of financial technology.
Contact Detail:
Richard Fairbank, Capital One Recruiting Team
StudySmarter Expert Advice🤫
We think this is how you could land Engineering Manager - Software & ML in London
✨Tip Number 1
Network like a pro! Reach out to current employees at the company you're eyeing, especially in engineering roles. A friendly chat can give you insider info and might even lead to a referral.
✨Tip Number 2
Show off your skills! Prepare a portfolio or a project that highlights your experience with software development and machine learning. This can really set you apart during interviews.
✨Tip Number 3
Practice makes perfect! Get comfortable with common interview questions for engineering managers. Think about how you’d handle team dynamics, project challenges, and tech trade-offs.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace Engineering Manager - Software & ML in London
Some tips for your application 🫡
Show Your Leadership Skills:When writing your application, make sure to highlight your experience in leading software engineering teams. We want to see how you've supported your team members in achieving their goals and how you’ve fostered a collaborative environment.
Demonstrate Technical Expertise:Don’t shy away from showcasing your technical skills! Mention your experience with RESTful API development and any cloud environments you've worked with. We’re keen on seeing how your background aligns with our needs in software and ML.
Connect the Dots with AI:Since this role involves machine learning, it’s important to convey your understanding of how AI fits into software development. Share any relevant experiences or projects that illustrate your grasp of model inference and data requirements.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates during the process!
How to prepare for a job interview at Richard Fairbank, Capital One
✨Know Your Tech Stack
Make sure you brush up on your knowledge of RESTful API development and the programming languages mentioned in the job description, like Java, Python, or .Net. Familiarity with AWS or Azure will also give you an edge, so be ready to discuss how you've used these technologies in past projects.
✨Showcase Your Leadership Skills
Prepare examples that highlight your experience in leading software engineering teams. Think about specific instances where you nurtured talent or helped your team achieve their goals. This is your chance to demonstrate your leadership excellence and collaborative mindset.
✨Understand AI Integration
Even if you're not a researcher, it's crucial to understand how AI fits into the software stack. Be prepared to discuss model inference, data requirements, and how to manage the non-deterministic nature of AI. This will show that you can bridge the gap between engineering and machine learning.
✨Be Ready for Strategic Discussions
Expect questions about making technical trade-offs between rapid experimentation and long-term architectural stability. Think through scenarios where you've had to balance these aspects in your previous roles, as this will demonstrate your strategic thinking abilities.
