Teams at Gigaton

Develop research methods for modeling, optimization, and control of complex industrial systems. Formulate research questions from live plant challenges, design rigorous experiments, incorporate physics and operating constraints, collaborate with engineering teams to deploy machine learning and control algorithms, evaluate production performance, investigate failures, and communicate findings. Focus areas include system identification, Bayesian optimization, probabilistic modeling, physics-informed machine learning, active learning, and model-based reinforcement learning.

Build reliable, well-tested platform software for AI-powered industrial control systems. Responsibilities include developing with Python and Terraform, supporting production systems, improving architecture, mentoring engineers, strengthening software practices across engineering and ML teams, and enhancing system quality, maintainability, and reliability. The role spans CI/CD, networking, plant-system data integration, IoT operating systems, and customer-facing control software.

Leads Gigaton’s Solutions team and owns deployment of AI systems across industrial customer plants. The role is responsible for scaling from 9 to 50 live plants, reducing deployment timelines, converting bespoke work into repeatable product capabilities, managing escalations, defining evaluation success, developing post-sales operations, and hiring and coaching a high-performing deployment organization. Requires customer leadership, hands-on deployment experience, and approximately 25% travel to global plants.