CEPTON (A Koito Group Company, Automotive Tier1), a leading intelligent lidar solution provider, is seeking a R&D Machine Learning Postdoctoral Researcher who is passionate about solving challenges related to advanced sensor technology to join us. Working as part of our brilliant R&D team, you will work in a dynamic and collaborative work environment for the advanced LiDAR product and sensor fusion system driven by the robust embedded system for automotive and non-automotive customers.
· Support the development and evaluation of machine-learning models for LiDAR-oriented ADA Sperception systems.
· Conduct systematic hyper parameter tuning, model benchmarking, and controlled experiments to improve model accuracy, robustness, and efficiency.
· Assist with model training, validation, and performance analysis, including identifying failure modes and documenting experimental results.
· Develop and maintain workflows for data collection, automated labeling, data cleaning, and dataset quality assessment.
· Analyze large-scale sensor datasets and help identify data gaps, edge cases, and representative training samples.
· Support work involving multimodal sensor data, including basic data alignment, synchronization, and fusion across LiDAR, cameras, and other sensors.
· Collaborate with research scientists and engineers to translate research ideas into reproducible experiments and working prototypes.
· PhD, recent PhD, or post doctoral research experience in physics, applied mathematics, robotics, computer science, machine learning, or a closely related quantitative field.
· Strong analytical skills and a foundation in scientific computing, mathematical modeling, or data-driven research.
· Familiarity with deep-learning fundamentals, model training, optimization, and experimental evaluation.
· Proficiency in Python and practical experience with PyTorch or TensorFlow.
· Experience working with large datasets, including data processing, visualization, quality analysis, and experiment tracking.
· Ability to conduct structured experiments, interpret results, and communicate findings clearly to a multidisciplinary team.
· Willingness to work with both machine-learning software and physical sensor data in a research and development environment.
Preferred Qualifications
· Experience with hyperparameter optimization, distributed experiments, or large-scale model evaluation.
· Experience using GPU servers ormulti-GPU systems for model training, inference, or batch data processing.
· Familiarity with automatedannotation, active learning, data mining, or dataset curation workflows.
· Hands-on experience with LiDAR,cameras, inertial sensors, or related sensing hardware.
· Familiarity with sensorcalibration, coordinate transformations, time synchronization, or PrecisionTime Protocol (PTP).
· Relevant research publications orproject experience in machine learning, computer vision, robotics,autonomous systems, or computational physics.
· Familiarity with Rust is aplus.
· Prior experience supportingresearch in ADAS, robotics, real-time systems, or physically embodiedmachine-learning applications is a plus.
Compensation: Hourly Rate $100/hour
We are an Equal Opportunity Employer and do not discriminate against applicants due to race, ethnicity, gender, veteran status, or on the basis of disability or any other federal, state or local protected class.
