At a Glance
- Tasks: Lead the ML team to develop innovative education solutions using unique data.
- Company: Exciting London startup transforming education with machine learning.
- Benefits: Unlimited learning budget, 25 days leave, and regular social events.
- Why this job: Join a groundbreaking project and make a real impact in education.
- Qualifications: Proven ML experience, leadership skills, and expertise in Python and PyTorch.
- Other info: Be part of a dynamic startup adventure with excellent growth opportunities.
The predicted salary is between 48000 - 72000 £ per year.
We are a small London startup with the ambition to change education with ML-powered tutoring. Our flagship product is a mobile application for teaching English to intermediate and advanced learners. We’re on the verge of solving one of the biggest challenges in education – making high-quality, personalised learning accessible to everyone. We are building a fundamental model for education - one that can accurately predict student knowledge and orchestrate lessons, adapting to the students' needs.
We’re looking for a Lead ML Engineer, with a proven track record of delivering ML models to production, to own the ML team in our growing company.
- Work with a vast amount of unique data - we have data from over 1M language tests, including text and voice data.
- Create brand new dictionaries, applicable to any context - right now it’s the English language, but in the future it could be other languages or even subjects such as Mathematics.
- Analyse large amounts of diverse data - including data from every movie, book, and song.
- Create new types of tests for language learners to gather more test results, analyse them, and build prediction models based on these results.
- Optimise and fine-tune machine learning models for performance, scalability, and accuracy.
- Lead our ‘AI Powered Education’ meet-up in London, building a network of ML specialists.
- Experience leading and mentoring other ML engineers.
- Complete end-to-end experience - from finding and cleaning data all the way to monitoring models in production.
- Experience taking complete ownership of entire ML systems and data pipelines in large-scale production systems.
- Experience building automated data pipelines.
- Strong understanding of CI/CD pipelines and automation tools for efficient model deployment and monitoring.
- Strong understanding of neural networks, CNNs, RNNs, LSTMs, and transformers.
- Expertise in Python and PyTorch.
- Can speak, or learning to speak, more than one language.
- Knowledge-sharing experience (tech talks, articles, YouTube videos, etc.).
- Experience using voice data in ML models.
- Lead an ML team that will become the leading specialists in ML-powered education.
A real-deal startup adventure: you’ll be hopping on a major project while it’s still in the works! Unlimited learning & development budget (courses, conferences, books etc.) Regular social events 25 days annual leave + public holidays Work from our London office.
If all the interviews are successful, we’d like to invite you to 2 or 3 paid trial days, or to complete a short project remotely with us, to learn what it’s like to work at Glite.
Lead Machine Learning Engineer in London employer: Glite Tech
Contact Detail:
Glite Tech Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Attend meet-ups and events related to machine learning and education tech. This is your chance to connect with industry folks, including us at StudySmarter, and show off your passion for ML-powered education.
✨Tip Number 2
Get hands-on! If you can, work on a small project that showcases your skills in ML. Share it on platforms like GitHub or even your own blog. We love seeing practical examples of what you can do!
✨Tip Number 3
Prepare for those interviews! Brush up on your knowledge of neural networks, CI/CD pipelines, and Python. We want to see that you’re not just a fit on paper but also ready to dive into the nitty-gritty of ML systems.
✨Tip Number 4
Apply through our website! It’s the best way to get noticed by our team. Plus, we love seeing candidates who take the initiative to reach out directly. Don’t miss out on this opportunity!
We think you need these skills to ace Lead Machine Learning Engineer in London
Some tips for your application 🫡
Show Your Passion for Education: When writing your application, let us know why you're excited about using ML to change education. Share any personal experiences or projects that highlight your commitment to making learning accessible and engaging.
Highlight Relevant Experience: Make sure to showcase your experience with ML models and data pipelines. We want to see examples of your work that demonstrate your ability to deliver models to production and lead a team effectively.
Be Authentic and Personal: Don’t be afraid to let your personality shine through in your application. We’re a small startup, and we value authenticity. Share your journey, your interests, and what makes you unique as a candidate.
Apply Through Our Website: To make sure your application gets the attention it deserves, apply directly through our website. It’s the best way for us to keep track of your application and get back to you quickly!
How to prepare for a job interview at Glite Tech
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts, especially neural networks, CNNs, RNNs, and transformers. Be ready to discuss your past projects in detail, particularly those where you delivered ML models to production.
✨Showcase Your Leadership Skills
Since this role involves leading a team, be prepared to share examples of how you've mentored other engineers. Talk about your experience in building a collaborative environment and how you’ve driven projects forward.
✨Get Familiar with Their Product
Dive into their mobile application for teaching English. Understand its features and think about how your expertise can enhance it. This shows genuine interest and helps you align your skills with their goals.
✨Prepare for Data Discussions
With a vast amount of unique data at play, be ready to discuss your experience with data pipelines and analysis. Bring examples of how you've optimised models for performance and scalability, and be prepared to brainstorm ideas for new types of tests.