Clarios is looking for a Machine Learning Engineer with leadership capabilities to architect and implement algorithmic solutions for their industrial IoT platform, guiding a team to deliver production-grade systems.
Requirements
- 5+ years of experience in machine learning and software engineering.
- Solid understanding of core machine learning and AI algorithms, including supervised and unsupervised learning, classification, regression, clustering, and deep learning techniques.
- Strong proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn), SQL and cloud platforms.
- Experience with time-series data
- Knowledge of MLOps tools (e.g., MLflow, Airflow, Docker, Kubernetes).
- Experience with one or more of signal processing, edge computing and physics-informed ML models.
Responsibilities
- Design and deploy models for predictive maintenance, anomaly detection, asset optimization, and time-series forecasting.
- Work with large-scale sensor data from connected devices.
- Develop robust data pipelines and real-time inference systems integrated with edge and cloud infrastructure.
- Lead the end-to-end technical execution of ML projects, from ideation to deployment.
- Mentor and support a team of ML and software engineers by defining and enforcing best practices in model development, testing, and deployment.
- Partner with product managers and domain experts to align technical solutions with business goals.
Other
- This is a remote position open to candidates located within the United States.
- Proven experience leading technical teams or projects in a production environment.
- Excellent communication and cross-functional collaboration skills.
- For residents of New York City, New York, California, and Washington state only, as required under applicable pay transparency laws, the expected salary range for this position if filled remotely is $140,000-$180,000.
- Employees are eligible to participate in Clarios’ variable pay program, subject to the program’s conditions and restrictions.
