At a Glance
- Tasks: Lead the development of backend systems for ML-driven applications and build scalable APIs.
- Company: Join Kingfisher, a passionate team dedicated to better homes and lives.
- Benefits: Enjoy a competitive benefits package and opportunities for career growth.
- Other info: Flexible hybrid work model with a focus on collaboration and inclusivity.
- Why this job: Shape the future of home improvement with cutting-edge machine learning technology.
- Qualifications: Strong Python experience, Django knowledge, and leadership skills in engineering.
The predicted salary is between 60000 - 80000 £ per year.
We’re Kingfisher, a team made up of over 74,000 passionate people who bring Kingfisher - and all our other brands: B&Q, Screwfix, Brico Depot, Castorama and Koctas to life. Guided by our purpose Better Homes. Better Lives. For Everyone. We believe a better world starts with better homes, and we work every day to make that a reality. Join us and help shape the future of home improvement.
We are the central Machine Learning and AI team supporting multiple retail brands within the Kingfisher group. Our engineers build and operate production ML systems that power use cases such as forecasting, recommendations, pricing optimisation and generative AI applications. The team includes around 50 engineers across machine learning, backend and frontend, working together to deliver full-stack ML-powered products.
Responsibilities
- You will lead the development of backend systems that power our ML-driven applications. This includes building APIs and services that expose machine learning models, integrating with data and ML pipelines, and ensuring systems are reliable and scalable in production.
- You will work closely with machine learning engineers and frontend engineers to deliver end-to-end products, while providing technical leadership to backend engineers in the team.
- Lead the design and development of backend systems supporting ML-powered products and applications.
- Build and maintain scalable APIs and services that integrate with machine learning systems.
- Ensure backend systems are reliable, maintainable and performant in production.
- Provide technical leadership to backend engineers, including code reviews, mentoring and setting engineering standards.
- Work with frontend engineers to deliver complete, user-facing products.
- Contribute to architectural and infrastructure decisions.
What you’ll bring
- Strong experience building backend systems in Python.
- Excellent knowledge of Django and experience designing production-grade APIs and services.
- Strong software engineering fundamentals, including system design and scalable architectures.
- Experience building and operating cloud-based services.
- Experience leading or mentoring engineers.
- Ability to work closely with cross‑functional teams, including ML and frontend engineers.
How We Work
We believe in flexibility and balance. Our hybrid model blends home working for focus with time spent connecting and collaborating - whether in our offices or at off‑site locations. On average within our Engineering team - 40% of your time involves in‑person collaboration. We value the perspectives new team members bring and encourage you to apply - even if you don’t meet 100% of the requirements.
What We Offer
An inclusive environment where your potential is limited only by your imagination. We encourage new ideas, support experimentation, and strive to create a workplace where everyone can be their best self. We also offer a competitive benefits package and plenty of opportunities to stretch and grow your career.
Diversity & Inclusion
Our customers come from all walks of life - and so do we. We’re committed to ensuring all colleagues, future colleagues, and applicants are treated equally, regardless of age, gender, marital or civil partnership status, ethnicity, culture, religion, belief, political opinion, disability, gender identity, gender expression, or sexual orientation.
Interested? Great, apply now and help us to Power the Possible.
StudySmarter Expert Advice🤫
We think this is how you could land Lead ML Platform Engineer
✨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 Women in Data® 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 Women in Data®.
✨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 Women in Data®.
✨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 Women in Data® 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 Lead ML Platform Engineer
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 Women in Data®.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Women in Data® 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 Women in Data®
✨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 Women in Data® 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.
