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- Tasks: Join us as a Machine Learning Engineer and build innovative solutions with cutting-edge technologies.
- Company: Builder.ai is a dynamic tech company focused on empowering creators to build software effortlessly.
- Benefits: Enjoy competitive pay, flexible work options, and a vibrant team culture.
- Why this job: Be part of a mission-driven team that values creativity and innovation in tech.
- Qualifications: Experience with knowledge graphs, Docker, Kubernetes, and scalable architectures is essential.
- Other info: This role offers a chance to work in London and make a real impact.
The predicted salary is between 36000 - 60000 £ per year.
This is a job posted by our partner Jooble
Below is a snippet of the job description. To read the full text, please click on the “Apply Now” link.
Required Skills
– Experience in building knowledge graphs and graph databases
– Experience working with events type data
– Experience with technologies such as Docker or Kubernetes
– Engineering at scale using production-level architectures
– Discretionary variable pay or commission scheme
108 E 16th Street, New York, NY 10003
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Machine Learning Engineer in London - Builder.ai - What would you Build- employer: Builder.ai - What would you Build?
Contact Detail:
Builder.ai - What would you Build? Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in London - Builder.ai - What would you Build-
✨Tip Number 1
Familiarize yourself with knowledge graphs and graph databases. Consider building a small project or contributing to open-source projects that utilize these technologies to showcase your skills.
✨Tip Number 2
Gain hands-on experience with Docker and Kubernetes. You can do this by setting up your own development environment or deploying a sample application to demonstrate your understanding of containerization and orchestration.
✨Tip Number 3
Understand the principles of engineering at scale. Research best practices for building production-level architectures and consider writing a blog post or creating a presentation to share your insights.
✨Tip Number 4
Network with professionals in the machine learning field. Attend meetups, webinars, or conferences to connect with others and learn about the latest trends and opportunities in the industry.
We think you need these skills to ace Machine Learning Engineer in London - Builder.ai - What would you Build-
Some tips for your application 🫡
Highlight Relevant Experience: Make sure to emphasize your experience in building knowledge graphs and working with event-type data. Use specific examples from your past work to demonstrate your expertise.
Showcase Technical Skills: Clearly list your proficiency with technologies such as Docker and Kubernetes. Mention any projects where you utilized these tools, especially in a production-level architecture.
Tailor Your CV: Customize your CV to align with the job description. Focus on your engineering skills at scale and any relevant achievements that showcase your ability to work with complex systems.
Craft a Compelling Cover Letter: Write a cover letter that not only outlines your qualifications but also expresses your enthusiasm for the role and the company. Explain why you are a great fit for Builder.ai and what you would build.
How to prepare for a job interview at Builder.ai - What would you Build?
✨Showcase Your Knowledge Graph Experience
Be prepared to discuss your experience with building knowledge graphs and graph databases. Bring specific examples of projects you've worked on, the challenges you faced, and how you overcame them.
✨Demonstrate Your Understanding of Event Data
Since the role involves working with event-type data, be ready to explain how you've handled such data in previous roles. Discuss any relevant technologies or methodologies you've used.
✨Familiarize Yourself with Docker and Kubernetes
Make sure you understand the basics of Docker and Kubernetes, as these are key technologies for the position. Be ready to discuss how you've utilized these tools in your past projects.
✨Discuss Production-Level Architectures
Prepare to talk about your experience with engineering at scale and production-level architectures. Highlight any specific systems you've designed or contributed to, and the impact they had on performance and scalability.
