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- Tasks: Develop predictive maintenance models using advanced imaging and sensor data.
- Company: Join a dynamic tech company at the forefront of innovation.
- Benefits: Enjoy flexible working options and exciting corporate perks.
- Why this job: Make a real impact in robotics while collaborating with talented professionals.
- Qualifications: Proficient in Python and PyTorch; experience with image-based ML models preferred.
- Other info: Opportunity to work with high-voltage equipment and enhance your skills.
The predicted salary is between 42000 - 84000 £ per year.
A growing technology company is seeking a Machine Learning Engineer to develop predictive maintenance models for robotic components. This role involves building models from scratch using optical, UV, and thermal imaging data, as well as environmental sensor data such as humidity, temperature, and pressure.
Key Responsibilities
- Develop machine learning models to detect anomalies and failure patterns in robotic components.
- Use Python and PyTorch to process real-time image and sensor data.
- Create synthetic datasets to enhance model training and failure mode analysis.
- Work with high-voltage equipment data to refine predictive capabilities.
- Work with manufacturers to improve equipment reliability through machine learning insights.
- Collaborate with an in-house data scientist to process and analyze ML outputs.
- Set up and optimize machine learning pipelines for real-world deployment.
Key Skills & Experience
- Proficiency in Python and PyTorch for model development.
- Experience working with image-based machine learning models, including optical, UV, and thermal imaging.
- Understanding of anomaly detection, predictive maintenance, and synthetic data generation.
- Experience with high-voltage equipment is a plus.
How to Apply
To learn more, apply now or send your CV to.
Machine Learning Engineer employer: Your Next Hire
Contact Detail:
Your Next Hire Recruiting Team
del@ynh.group
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with the latest advancements in machine learning, particularly in predictive maintenance and anomaly detection. This will not only help you during interviews but also demonstrate your genuine interest in the field.
✨Tip Number 2
Build a portfolio showcasing your projects related to image-based machine learning models. Include examples where you've used Python and PyTorch, as this will give you an edge over other candidates.
✨Tip Number 3
Network with professionals in the industry, especially those who work with robotic components or high-voltage equipment. Attend relevant meetups or webinars to make connections that could lead to referrals.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges and machine learning problems. Focus on real-time data processing scenarios, as these are likely to come up in discussions about the role.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python and PyTorch, as well as any relevant projects involving image-based machine learning models. Emphasise your skills in anomaly detection and predictive maintenance.
Craft a Strong Cover Letter: In your cover letter, explain why you're passionate about machine learning and how your background aligns with the company's goals. Mention specific experiences where you've developed models or worked with high-voltage equipment.
Showcase Relevant Projects: If you have worked on projects involving synthetic data generation or real-time data processing, be sure to include these in your application. Provide links to any relevant GitHub repositories or portfolios.
Highlight Collaboration Skills: Since the role involves working with an in-house data scientist and manufacturers, mention any past experiences where you've successfully collaborated with others to achieve project goals.
How to prepare for a job interview at Your Next Hire
✨Showcase Your Technical Skills
Be prepared to discuss your proficiency in Python and PyTorch. Bring examples of past projects where you've developed machine learning models, especially those involving image-based data. This will demonstrate your hands-on experience and technical expertise.
✨Understand the Role of Predictive Maintenance
Familiarise yourself with predictive maintenance concepts and how they apply to robotic components. Be ready to explain how you would approach anomaly detection and failure pattern analysis, as this is a key responsibility of the role.
✨Discuss Data Processing Techniques
Since the role involves creating synthetic datasets, be prepared to talk about your experience with data processing and synthetic data generation. Highlight any relevant techniques or tools you've used to enhance model training.
✨Collaborate and Communicate
Emphasise your ability to work collaboratively, especially with data scientists and manufacturers. Share examples of how you've successfully communicated complex technical insights to non-technical stakeholders, as this will be crucial for improving equipment reliability.
