Highlights
Transforming production lines into intelligent systems; working on cutting-edge ML projects; fully remote or Zurich-based roles available.
Description
Job Summary
pAt Forgis, we're transforming manufacturing plants into intelligent ecosystems with our advanced orchestration platform. As a Machine Learning Engineer, you'll work at the forefront of predictive maintenance and real-time data analysis to optimize plant performance.
Responsibilities
- Build and train machine learning models on production data for failure prediction, anomaly detection, and process optimization.
- Iterate models against live plant data to ensure continuous improvement in accuracy and reliability.
- Deploy trained models into the Forgis platform, ensuring they run seamlessly with real-time plant equipment data.
- Conduct thorough validation of models on actual production lines before deployment for decision-making purposes.
Required Skills
- Machine Learning
- Predictive Maintenance
- Anomaly Detection
- Data Analysis
- Model Deployment
Required Skills Explained
- Strong proficiency in machine learning frameworks and languages such as Python, R, or MATLAB.
- Experience with time-series analysis and sensor data processing.
- A solid understanding of model training, evaluation metrics, and deployment strategies.
- Familiarity with cloud platforms for deploying ML models (e.g., AWS, GCP).
- Knowledge in areas like predictive maintenance, anomaly detection, or process optimization using ML techniques.
Who is this for
pThis role is perfect for professionals with a strong background in machine learning, especially those who have experience deploying models in real-world industrial settings. A passion for continuous improvement and a knack for problem-solving are key.
Why This Job is a Good Opportunity
ulliTo work at the forefront of intelligent manufacturing technology where your ML expertise will have direct real-world impact.liThe opportunity to collaborate with cutting-edge digital engineers and contribute to innovative solutions in industrial automation.liFlexible working options, including remote or on-site in Zurich, Switzerland, providing a good work-life balance.liAccess to a supportive community within the company's Slack channel for ongoing learning and professional development.liPotential for growth with opportunities to lead projects and mentor junior team members.
Interview Preparation Tips
- Review real-world applications of ML in industrial settings, especially predictive maintenance and anomaly detection.
- Prepare examples of your past work where you successfully deployed machine learning models into production.
- Familiarize yourself with the company's products and services to demonstrate genuine interest.
- Be ready to discuss your experience with time-series data analysis and sensor data processing.
- PRACTICE: Simulate common interview questions on model training, evaluation, and deployment.
Career Growth in This Role
pThe role offers significant opportunities for growth as a Machine Learning Engineer at Forgis. You can advance by leading more complex projects that impact larger parts of the manufacturing process. There are also opportunities to transition into management roles or specialized technical leadership positions focused on specific areas like predictive maintenance or sensor data analysis.pThe company culture values innovation and continuous learning, providing ample chances for personal development through training programs, conferences, and collaborations with industry experts.
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Skills
Frequently Asked Questions
What kind of projects will I be working on?You’ll develop and deploy machine learning models that predict failures, detect anomalies, and optimize production processes in real-time.
Is this role fully remote or based in Zurich?The role can be done either fully remote or from our office in Zurich, Switzerland. Both options are available.
What kind of support will I get for visa sponsorship and relocation?Forgis supports visa sponsorship and relocation, so tell us your current location and we’ll assist you through the process.
