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- Tasks: Join a dynamic team to design and train cutting-edge machine learning models for robotics.
- Company: A well-established robotics start-up in the vibrant London area.
- Benefits: Enjoy a collaborative work environment with opportunities for growth and innovation.
- Why this job: Be part of a driven team making impactful advancements in robotics technology.
- Qualifications: Experience with CNNs, RNNs, Python, and a strong mathematics background required.
- Other info: Long-term opportunity with a focus on teamwork and problem-solving.
The predicted salary is between 36000 - 60000 £ per year.
Well-established robotics start-up is looking for a ML Engineer to join their team on-site in London area. Check out the list of requirements and get in touch with me to review the details around this offer:
- Experience with:
- Designing CNNs, RNNs
- Training using gradient and RL based learning
- Pytorch preferably, Tensorflow and Keras OK
- 3D physics simulations
- Python
- Working in 3D
- Computer graphics concepts: meshes, point clouds, rendering
- Computer vision concepts
- Control theory
- Kalman filters
It's a long term opportunity to work in a very collaborative and driven team in the heart of London on robotics solutions.
Machine Learning Engineer employer: Proactive Global
Contact Detail:
Proactive Global Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Make sure to showcase your experience with CNNs and RNNs in your discussions. Be prepared to explain how you've designed and trained these models, as this will demonstrate your technical expertise.
✨Tip Number 2
Familiarise yourself with the latest advancements in 3D physics simulations and computer graphics concepts. Being able to discuss these topics confidently will show that you're not just knowledgeable but also passionate about the field.
✨Tip Number 3
Prepare examples from your portfolio that highlight your problem-solving skills and analytical thinking. Discussing specific challenges you've overcome in previous projects can set you apart from other candidates.
✨Tip Number 4
Since communication is key in a collaborative environment, practice explaining complex AI models in simple terms. This will help you convey your ideas effectively during interviews and demonstrate your ability to work within a team.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, particularly with CNNs and RNNs. Include any projects that demonstrate your skills in Python, Pytorch, and 3D physics simulations.
Craft a Strong Cover Letter: Write a cover letter that showcases your passion for robotics and machine learning. Mention specific experiences that align with the job requirements, such as your familiarity with computer graphics concepts and collaborative work.
Showcase Your Portfolio: Include a link to your portfolio or attach examples of previous projects that illustrate your problem-solving skills and analytical thinking. Highlight any work related to control theory or Kalman filters if applicable.
Prepare for Technical Questions: Anticipate technical questions related to machine learning and computer vision during the interview process. Be ready to discuss your approach to designing models and solving complex problems.
How to prepare for a job interview at Proactive Global
✨Showcase Your Portfolio
Make sure to bring along a portfolio of your previous projects or designs. This is your chance to demonstrate your skills in machine learning and computer vision, so highlight any relevant work that showcases your experience with CNNs, RNNs, and 3D physics simulations.
✨Brush Up on Key Concepts
Before the interview, review key concepts in computer graphics and computer vision. Be prepared to discuss meshes, point clouds, and rendering techniques, as well as how they relate to the projects you've worked on.
✨Prepare for Technical Questions
Expect technical questions related to your experience with Pytorch, Tensorflow, and Keras. Be ready to explain your approach to training models using gradient and reinforcement learning, and how you would apply these techniques in real-world scenarios.
✨Demonstrate Team Collaboration Skills
Since this role involves working in a multidisciplinary team, be prepared to discuss your experiences collaborating with others. Highlight examples where your communication skills helped present complex AI models effectively to non-technical team members.
