Innovative Refrigeration Systems, Inc. is looking to develop advanced machine learning models that optimize energy efficiency across large-scale industrial refrigeration and cold storage facilities to create a more sustainable future for their Fortune 1000 clients powering America's food supply chain.
Requirements
- Minimum 2 years of hands-on experience building and optimizing ML systems.
- Strong understanding of machine learning theory and practical model deployment.
- Experience working in cloud ML environments (AWS, Azure, or GCP).
- Proficient in ML tools such as Python, TensorFlow, Scikit-learn, or R.
- Prior software engineering experience building backend systems around ML models.
Responsibilities
- Design, develop, and deploy ML-driven optimization systems that integrate with industrial PLC and IoT data streams.
- Build physics-based and reinforcement learning models to improve energy efficiency and temperature safety in industrial refrigeration systems.
- Fine-tune, test, and deploy machine learning models to production environments.
- Analyze large datasets from industrial sensors, controls, and process systems.
- Develop predictive models for maintenance and performance optimization.
- Document algorithms, workflows, and performance metrics for ongoing improvement.
Other
- Bachelor’s degree in Computer Science, Computer Information Systems, Data Science, Mechanical, Chemical, or Electrical Engineering (or equivalent experience).
- Excellent problem-solving and collaboration skills.
- Self-starter with the ability to learn mechanical engineering principles related to refrigeration and thermodynamics.
- Must be able to remain in a stationary position (seated or standing) for extended periods.
- Occasionally may need to lift or carry items up to 25 pounds (e.g., office supplies, small equipment).
- Master’s or PhD in Computer Science, Mathematics, or Engineering.
- Background in energy optimization, predictive maintenance, thermodynamics, or industrial refrigeration.
