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- Tasks: Lead innovative ML research and design cutting-edge architectures for multimodal AI.
- Company: Join Miro, a forward-thinking company at the forefront of AI technology.
- Benefits: Enjoy competitive global benefits in a supportive and collaborative environment.
- Other info: Be part of a dynamic team driving the future of AI.
- Why this job: Make a real impact by bridging theory with production systems in AI.
- Qualifications: PhD or significant industry experience in relevant fields; expertise in PyTorch or JAX.
The predicted salary is between 80000 - 100000 £ per year.
Miro is seeking a Lead Research Scientist to drive architectural decisions in their Machine Learning organization. This role involves designing innovative architectures that integrate multimodal inputs and bridging theory with production systems.
Candidates should have a PhD in relevant fields or significant industry experience, along with expertise in PyTorch or JAX. Additional skills in generative AI and engineering rigor are crucial.
The position offers competitive global benefits in a supportive environment.
Lead ML Research Scientist — Multimodal AI at Scale employer: Miro
Contact Detail:
Miro Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead ML Research Scientist — Multimodal AI at Scale
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Miro or similar companies. A friendly chat can sometimes lead to opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! If you’ve got projects or research that highlight your expertise in multimodal AI, PyTorch, or JAX, make sure to showcase them. A portfolio can really set you apart from the crowd.
✨Tip Number 3
Prepare for the interview by brushing up on both technical and theoretical aspects of ML. Be ready to discuss how you’d approach architectural decisions and integrate multimodal inputs in real-world applications.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take that extra step to connect with us directly.
We think you need these skills to ace Lead ML Research Scientist — Multimodal AI at Scale
Some tips for your application 🫡
Show Off Your Expertise: Make sure to highlight your PhD or relevant industry experience in your application. We want to see how your background aligns with the role, especially your skills in PyTorch or JAX.
Be Innovative: Since this role is all about designing innovative architectures, don’t hesitate to share any unique projects or ideas you've worked on. We love creativity and fresh perspectives!
Connect Theory to Practice: We’re looking for someone who can bridge theory with production systems. In your application, give examples of how you’ve successfully applied theoretical concepts in real-world scenarios.
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 Miro
✨Know Your Multimodal AI Inside Out
Make sure you’re well-versed in the latest trends and techniques in multimodal AI. Brush up on how different data types can be integrated and be ready to discuss your past experiences with these architectures.
✨Showcase Your Technical Skills
Since expertise in PyTorch or JAX is crucial, prepare to demonstrate your proficiency. Bring examples of projects where you've used these frameworks, and be ready to solve a coding challenge during the interview.
✨Bridge Theory with Practice
Miro is looking for someone who can connect theoretical concepts with real-world applications. Be prepared to discuss how you've successfully implemented research findings into production systems in your previous roles.
✨Highlight Your Generative AI Knowledge
Generative AI is a key area for this role, so make sure you can talk about your experience with it. Discuss any relevant projects or research, and be ready to share your thoughts on future trends in generative models.