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For Companies At a Glance
- Tasks: Join us as a Machine Learning Engineer to revolutionise robotics with cutting-edge AI systems.
- Company: Be part of a stealth-mode London startup transforming industrial automation through generative AI.
- Benefits: Enjoy flexible working options and the chance to work on groundbreaking technology.
- Why this job: Contribute to impactful projects that expand what robots can achieve across various industries.
- Qualifications: Advanced degree in Computer Science, Robotics, or related field; strong ML research background required.
- Other info: Work with top-tier scientists and utilise state-of-the-art tools in a collaborative environment.
The predicted salary is between 48000 - 72000 £ per year.
Please note: This is an external role with one of our valued customers, not a direct position with our company.
About the Company
The Company is a London-based startup currently in stealth mode which is pioneering the application of generative AI to industrial robotics. The Company is developing its own generative AI models along with an integrated platform and related tools to unlock intelligent automation using existing, proven robotic hardware. The Company's mission is to radically expand what robots can do in industrial and commercial environments, making automation more accessible, flexible, and impactful across multiple industries.
About the Role
The company is seeking a Machine Learning Engineer to implement and integrate cutting-edge AI systems for robotics applications. You will research and implement advanced learning algorithms, build scalable data pipelines, and translate breakthrough research into production systems. This role offers the opportunity to work on foundation models for robotics, multi-embodiment learning, and the infrastructure that will accelerate robotics development across industries.
Key Responsibilities
- Translate research to production by implementing state-of-the-art algorithms from top-tier conferences into deployed systems.
- Train deep learning models that handle multi-modal sensor data from different robot configurations and manufacturers.
- Collaborate with senior scientists to refine existing algorithms, suggesting improvements to maximise success rate on target applications.
- Utilise existing training infrastructure leveraging multi-GPU systems and distributed computing for large-scale model training.
- Create experimentation frameworks for large-scale evaluation and testing of robot learning approaches.
Required Skills
- Advanced degree (PhD, Master's, or equivalent experience) in Computer Science, Robotics, Machine Learning, or related field.
- Strong research background with publications at venues like CoRL, ICRA, RSS, ICML, ICLR, NeurIPS, or similar.
- Deep ML expertise in PyTorch or TensorFlow with experience in distributed training and optimisation.
- Robotics learning experience with practical knowledge of reinforcement learning and/or imitation learning.
- Production software skills with ability to build scalable, maintainable systems beyond research prototypes.
- Cloud infrastructure experience with platforms like AWS, GCP, or Azure and containerisation technologies.
Preferred Skills
- Multi-robot systems experience and cross-embodiment learning approaches.
- MLOps expertise including experiment tracking, model versioning, and deployment pipelines.
- Computer vision and sensor fusion for robotics applications.
- Real-world deployment experience with robot data collection and production model serving.
- Robotics middleware experience with ROS/ROS2 or similar frameworks.
- Familiarity with simulation environments and synthetic data generation.
Machine Learning Engineer: Robotics employer: Neuraco
Contact Detail:
Neuraco Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer: Robotics
✨Tip Number 1
Familiarise yourself with the latest research in generative AI and robotics. Since the company is pioneering this field, having a solid understanding of recent advancements and being able to discuss them will show your genuine interest and expertise.
✨Tip Number 2
Network with professionals in the robotics and machine learning community. Attend relevant conferences or meetups where you can connect with industry experts, as this could lead to valuable insights and potential referrals for the position.
✨Tip Number 3
Demonstrate your practical experience with multi-GPU systems and distributed computing. Be prepared to discuss specific projects where you've implemented these technologies, as hands-on experience is crucial for this role.
✨Tip Number 4
Showcase your knowledge of MLOps practices. Understanding experiment tracking, model versioning, and deployment pipelines will set you apart, as these skills are essential for translating research into production systems.
We think you need these skills to ace Machine Learning Engineer: Robotics
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, robotics, and any specific projects that align with the job description. Emphasise your advanced degree and any publications in top-tier conferences.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for generative AI and robotics. Mention specific skills like your expertise in PyTorch or TensorFlow, and how they relate to the role. Be sure to express your enthusiasm for the company's mission.
Showcase Relevant Projects: Include a section in your application that details any relevant projects you've worked on, especially those involving multi-modal sensor data, reinforcement learning, or cloud infrastructure. This will demonstrate your practical experience and problem-solving skills.
Proofread and Edit: Before submitting your application, carefully proofread all documents for spelling and grammatical errors. A polished application reflects your attention to detail and professionalism, which are crucial in technical roles.
How to prepare for a job interview at Neuraco
✨Showcase Your Research Background
Make sure to highlight your research experience, especially any publications in top-tier conferences like NeurIPS or ICML. Discussing specific projects can demonstrate your depth of knowledge and passion for the field.
✨Demonstrate Technical Proficiency
Be prepared to discuss your expertise in frameworks like PyTorch or TensorFlow. You might be asked to solve a technical problem on the spot, so brush up on your coding skills and be ready to explain your thought process.
✨Discuss Real-World Applications
Since the role involves translating research into production, share examples of how you've successfully implemented algorithms in real-world scenarios. This could include any experience with cloud platforms or robotics middleware.
✨Prepare for Collaborative Questions
Collaboration is key in this role, so expect questions about teamwork and communication. Think of examples where you worked with others to refine algorithms or tackle complex problems, showcasing your ability to contribute to a team environment.
