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- Tasks: Lead the development of scalable ML platforms and turn research into production systems.
- Company: Join a renowned AI company transforming cutting-edge research into real-world applications.
- Benefits: Enjoy hybrid work, competitive salary, and opportunities for professional growth.
- Why this job: Shape the future of AI technology while working on impactful projects.
- Qualifications: Experience in backend development and cloud-based systems; leadership skills are a plus.
- Other info: Dynamic team environment with a focus on innovation and collaboration.
The predicted salary is between 48000 - 72000 £ per year.
Areas: ML platform, inference infrastructure, backend development
Based: Zone 1, Central London
Hybrid: 3 days a week in-office
Workonomics is partnering with an AI company investing heavily in the platform behind its products. They’re focused on turning cutting-edge ML research into a reliable, scalable platform used by millions.
You’ll be responsible for taking research prototypes and turning them into production-grade inference systems, built in Python, running on GPUs, deployed via Kubernetes, and operating under real consumer traffic where latency, cost, and reliability all matter. You’ll have influence over architecture, standards, and team direction.
What they’re looking for:
- A track record of owning, building, and scaling complex platforms from design to production
- Backend development expertise focused on API design and observability / monitoring
- Comfort with cloud-based distributed systems
- Hands-on technical leadership experience
- Interest in / exposure to ML model serving and / or ML platform tooling
This is not a research role, and not a pure people-management position. It’s for engineers who enjoy owning complex systems end-to-end and shaping how ambitious technology works in the real world.
If this sounds like you, apply to learn more about the company and role.
Engineering Team Lead employer: Workonomics
Contact Detail:
Workonomics Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Engineering Team Lead
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to backend development and ML platforms. This gives potential employers a taste of what you can do beyond your CV.
✨Tip Number 3
Prepare for interviews by brushing up on technical questions and system design scenarios. Practice explaining your thought process clearly, as this will demonstrate your hands-on experience and leadership capabilities.
✨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 the role and the company.
We think you need these skills to ace Engineering Team Lead
Some tips for your application 🫡
Show Your Passion for ML: When you're writing your application, let us see your enthusiasm for machine learning and backend development. Share any relevant projects or experiences that highlight your skills in these areas.
Be Clear and Concise: We appreciate clarity! Make sure your application is easy to read and straight to the point. Use bullet points where necessary to break down your achievements and skills.
Tailor Your Application: Don’t just send a generic application. Tailor it to the role of Engineering Team Lead by highlighting your experience with complex platforms and technical leadership. Show us why you’re the perfect fit!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Workonomics
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python, Kubernetes, and cloud-based systems. Brush up on your knowledge of API design and observability, as these will likely come up during technical discussions.
✨Showcase Your Leadership Skills
Prepare examples that highlight your hands-on technical leadership experience. Think about times when you’ve influenced architecture or standards within a team. Be ready to discuss how you’ve taken ownership of complex systems and driven them from design to production.
✨Understand the Business Impact
Since this role involves turning ML research into scalable platforms, be prepared to discuss how your work can impact real consumer traffic. Show that you understand the importance of latency, cost, and reliability in production environments.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s ML platform and future direction. This shows your genuine interest in the role and helps you gauge if the company aligns with your career goals. Ask about their challenges in scaling platforms or how they measure success in their projects.
