We are looking for a data/ML engineer to design and train predictive models that analyze factory workloads and optimize machine scheduling. This role blends traditional time-series processing with simulation feedback and IoT signal processing. Responsibilities • Design pipelines to ingest telemetry and sensor data from physical assets; • Build predictive models to optimize CNC/robotic arm utilization and scheduling; • Work with the Simulation Engineer to validate model impact in virtual environments; • Implement dynamic feedback loops between real and simulated systems; • Create dashboards for performance metrics and anomaly detection. Requirements • 4+ years working with time-series or predictive ML models; • Strong experience in Python, Pandas, Scikit-learn, PyTorch or TensorFlow; • Familiarity with Kafka, Spark, or cloud-based streaming systems; • Understanding of factory scheduling, queueing theory, or optimization is a plus; • Experience collaborating with simulation or robotics teams is highly valued. Nice to have Agile, quick learner, good collaborator and communicator. We offer • Opportunity to work on bleeding-edge projects • Work with a highly motivated and dedicated team • Competitive salary • Flexible schedule • Professional development opportunities About Us Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.