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- Tasks: Shape the future of software development with AI and ML technologies.
- Company: Join JetBrains, a leader in innovative software solutions.
- Benefits: Enjoy competitive salary and startup-like autonomy.
- Other info: Be part of a dynamic team driving cutting-edge technology.
- Why this job: Make a real impact by defining the research agenda in ML.
- Qualifications: 5+ years in ML/AI systems and strong Python skills required.
The predicted salary is between 60000 - 80000 £ per year.
JetBrains is looking for a top-class ML Engineer to shape the future of software development through AI and ML technologies. You will define the research agenda and own the AI engineering stack.
Responsibilities include:
- Designing LLM solutions
- Establishing MLOps practices
The ideal candidate should have at least five years of experience in ML/AI systems, hands-on LLM experience, and strong Python skills.
We offer a competitive salary and startup-like autonomy.
Founding ML Engineer: Build a Living Semantic Layer employer: JetBrains
Contact Detail:
JetBrains Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Founding ML Engineer: Build a Living Semantic Layer
✨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 showcasing your ML projects, especially those involving LLMs. This is your chance to demonstrate your hands-on experience and Python prowess, so make it shine!
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common ML interview questions and be ready to discuss your past projects in detail. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! We love seeing applications directly from candidates who are excited about joining us. It shows initiative and helps us get to know you better right from the start.
We think you need these skills to ace Founding ML Engineer: Build a Living Semantic Layer
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with ML/AI systems and LLMs in your application. We want to see your strong Python skills shine through, so don’t hold back on showcasing your projects or any relevant work you've done!
Tailor Your Application: Take a moment to customise your application for the Founding ML Engineer role. Mention how your background aligns with our mission at JetBrains and how you can contribute to shaping the future of software development through AI and ML technologies.
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 qualifications and experiences without unnecessary fluff.
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 to join our team!
How to prepare for a job interview at JetBrains
✨Know Your ML Fundamentals
Make sure you brush up on your machine learning fundamentals. Be prepared to discuss algorithms, model evaluation metrics, and the latest trends in AI. This will show that you have a solid foundation and can contribute to shaping the research agenda.
✨Showcase Your LLM Experience
Since hands-on experience with LLMs is crucial for this role, come ready to share specific projects you've worked on. Discuss the challenges you faced, how you overcame them, and the impact of your work. This will demonstrate your practical knowledge and problem-solving skills.
✨Demonstrate Your Python Proficiency
As strong Python skills are essential, be prepared to talk about your coding experience. You might even be asked to solve a coding challenge during the interview. Practise common Python problems and be ready to explain your thought process clearly.
✨Understand MLOps Practices
Familiarise yourself with MLOps practices, as establishing these will be part of your responsibilities. Be ready to discuss how you would implement MLOps in a project and the tools you prefer. This shows that you’re not just a coder but also understand the operational side of ML.