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- Tasks: Build and enhance ML research and deployment pipelines with innovative solutions.
- Company: Join a forward-thinking team dedicated to machine learning excellence.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with exciting projects and career advancement potential.
- Why this job: Be at the forefront of ML technology and tackle complex challenges head-on.
- Qualifications: Experience in machine learning and strong problem-solving skills required.
The predicted salary is between 36000 - 60000 £ per year.
We are looking for exceptional engineers to help us build out a mature ML research and deploy pipeline as part of our ML Workflows team. This is an exciting role. You will develop greenfield solutions to meet highly complex ML interdependency requirements. Where off-the-shelf tools fall short, you’ll build custom solutions to define best practice across quantitative research.
Future projects include:
- Implementing best practice feature and model stores
- Properly versioning features, data and models
- Improving inference compute utilisation via model serving
- CI/CD for ML
- Reliably fitting models with complex job dependency graphs
- Robustness in production, including validation and monitoring
Machine Learning Workflow Engineer in London employer: Europython
Contact Detail:
Europython Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Workflow Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the ML community, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML workflows. This is your chance to demonstrate how you tackle complex problems and build custom solutions that align with our needs.
✨Tip Number 3
Prepare for technical interviews by brushing up on your ML concepts and coding skills. Practice solving problems that involve model serving, CI/CD, and job dependency graphs. We want to see how you think and approach challenges!
✨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 us you’re genuinely interested in joining our team and contributing to our exciting projects.
We think you need these skills to ace Machine Learning Workflow Engineer in London
Some tips for your application 🫡
Show Your Passion for ML: When writing your application, let your enthusiasm for machine learning shine through! We want to see how excited you are about building innovative solutions and tackling complex challenges in the field.
Tailor Your Experience: Make sure to highlight your relevant experience with ML workflows and custom solutions. We love seeing specific examples of how you've tackled similar problems or projects in the past, so don’t hold back!
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon unless it’s necessary. Make it easy for us to see why you’re a great fit for the role!
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 this exciting opportunity on our ML Workflows team!
How to prepare for a job interview at Europython
✨Know Your ML Pipelines
Make sure you understand the intricacies of machine learning workflows. Brush up on your knowledge of feature stores, model versioning, and CI/CD processes. Being able to discuss these topics confidently will show that you're not just familiar with the theory but can also apply it in practice.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous projects, especially those involving custom solutions. Use the STAR method (Situation, Task, Action, Result) to structure your answers, highlighting how you tackled complex interdependencies and improved processes.
✨Demonstrate Your Technical Proficiency
Be ready to dive into technical discussions about model serving and inference compute utilisation. Brush up on relevant tools and frameworks, and if possible, bring examples of your work or projects that illustrate your expertise in building robust ML systems.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's ML workflows and future projects. This shows your genuine interest in the role and helps you gauge if the company’s goals align with your career aspirations. Questions about their approach to validation and monitoring in production can spark engaging conversations.