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- Tasks: Drive AI strategies and build scalable ML systems in a dynamic environment.
- Company: Leading retail intelligence platform with a supportive culture.
- Benefits: Flexible working options and opportunities for technical mentorship.
- Why this job: Own the ML lifecycle and make a real impact in AI development.
- Qualifications: Strong Python skills and experience with PyTorch or TensorFlow.
- Other info: Join a collaborative team and grow your career in AI.
The predicted salary is between 48000 - 72000 £ per year.
A leading retail intelligence platform in Greater London seeks a Staff Machine Learning Engineer to drive AI strategies and build complex, scalable systems. You will own the entire ML lifecycle, engage in cross-functional collaboration, and lead technical mentorship within the team.
Ideal candidates will possess a strong proficiency in Python and frameworks like PyTorch or TensorFlow, along with experience in deploying production models.
The company offers a supportive culture with flexible working options.
Staff ML Engineer - AI Systems & MLOps (Remote) in London employer: Edited
Contact Detail:
Edited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Staff ML Engineer - AI Systems & MLOps (Remote) in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working at the company you're eyeing. A friendly chat can give you insider info and maybe even a referral!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those using Python, PyTorch, or TensorFlow. This will help you stand out and demonstrate your hands-on experience.
✨Tip Number 3
Prepare for technical interviews by brushing up on your ML concepts and coding skills. Practice common algorithms and be ready to discuss your past projects in detail – they want to see how you think!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Staff ML Engineer - AI Systems & MLOps (Remote) in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your proficiency in Python and any experience you have with frameworks like PyTorch or TensorFlow. We want to see how your skills align with the role, so don’t hold back!
Tailor Your Application: Take a moment to customise your application for this specific role. Mention your experience with the ML lifecycle and any cross-functional collaboration you've done. It helps us see how you fit into our team!
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured applications that get straight to the heart of your experience and achievements.
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’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Edited
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts, especially around the entire ML lifecycle. Be ready to discuss your experience with Python and frameworks like PyTorch or TensorFlow, as well as any production models you've deployed.
✨Showcase Your Collaboration Skills
Since this role involves cross-functional collaboration, think of examples where you've successfully worked with other teams. Be prepared to share how you’ve communicated complex technical ideas to non-technical stakeholders.
✨Prepare for Technical Questions
Expect some deep dives into your technical expertise. Practice explaining your thought process when solving ML problems, and be ready to tackle coding challenges that may come up during the interview.
✨Emphasise Mentorship Experience
As a Staff ML Engineer, you'll likely be mentoring others. Think about times you've guided colleagues or led projects, and be ready to discuss your approach to mentorship and fostering a supportive team culture.
