onsite
Machine Learning Engineer - Gigaton
ML Engineer
Lead the design and deployment of AI‑driven control systems that optimize heavy‑industry processes, reducing emissions and boosting efficiency in real time.
About the role
Key Responsibilities
- Develop end‑to‑end ML pipelines for real‑time process control in cement, steel, and glass manufacturing.
- Design and train deep learning and reinforcement learning models that capture complex physics and operational constraints.
- Collaborate with data engineers to ingest, clean, and feature‑engineer high‑frequency sensor streams.
- Deploy models to edge and cloud (AWS) environments, ensuring low latency and high reliability.
- Monitor model performance, conduct drift analysis, and iterate on model improvements.
Requirements
- 5+ years of experience building production ML systems in industrial settings.
- Proficiency in Python, PyTorch/TensorFlow, and experience with reinforcement learning frameworks.
- Strong background in data engineering, time‑series analysis, and cloud deployment (AWS, SageMaker).
- Excellent problem‑solving skills and ability to translate domain physics into ML solutions.
- Effective communicator who can work cross‑functionally with scientists, engineers, and operators.
Skills
pythonmachine learningdeep learningreinforcement learningpytorchaws