R&D Machine Learning Research Scientist
$180k - $220kCepton
CEPTON (A Koito Group Company, Automotive Tier1), a leading intelligent lidar solution provider, is seeking a seasoned R&D Machine Learning Research Scientist 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. Here at CEPTON , we use technology to make some difficult problems easy and some impossible tasks become feasible. We value one's ability to learn higher than the knowledge one has acquired in the past. We enjoy solving hard problems together. As we are at the cutting edge of technological advancement, join us if you are willing to work with like-minded people to face the challenges posed by the new era of technology, together with opportunities to work with strong industry leaders in the fields of automotive OEM, ADAS, and AI applications. Responsibilities Design novel neural-network architectures from first principles for LiDAR-oriented ADAS perception systems . Develop, train, and systematically optimize deep-learning models , including architecture exploration, hyperparameter tuning , and rigorous experimental evaluation. Develop scalable approaches for automated data labeling , data quality improvement, and efficient use of large sensor datasets. Apply physical intuition and first-principles reasoning to the modeling of dynamic, three-dimensional environments. Work across machine learning, sensing, and embedded-system boundaries to ensure that research methods are grounded in real-world system behavior. Investigate multimodal sensor fusion and the effects of sensor timing, synchronization, latency, and data alignment on model performance. Qualifications PhD or postdoctoral research experience in physics, AP, EE, applied mathematics, CS, machine learning, or a closely related technical field. Strong foundation in physics, dynamical systems, or mathematical modeling of physical systems . Demonstrated ability to design neural-network architectures from scratch rather than relying solely on established or off-the-shelf models. Strong understanding of deep-learning fundamentals, training dynamics, optimization methods, and experimental methodology . Proficiency in Python and hands-on experience with PyTorch or TensorFlow . Working knowledge of hardware-integrated sensing systems, including Precision Time Protocol (PTP), multimodal sensor synchronization, and sensor data fusion . Ability to conduct independent research , formulate ambiguous technical problems, and communicate findings clearly to multidisciplinary teams. Preferred Qualifications Relevant publications in machine learning, computer vision, robotics, autonomous systems, physics-informed learning, or related fields. Hands-on experience with LiDAR, cameras, inertial sensors, or other sensing hardware used in robotics or ADAS applications. Experience with sensor calibration, coordinate transformations, time alignment, and synchronization diagnostics . Experience training and evaluating models on GPU servers or multi-GPU systems , including distributed or large-scale training workflows. Familiarity with Rust and an interest in implementing or integrating research methods in performance-sensitive systems. Experience developing automated annotation, active learning, data mining, or dataset curation systems . Prior experience applying machine learning to safety-critical, real-time, or physically embodied systems . Package: Salary range $180k~220k Performance based bonus program eligibility
- Comprehensive employee benefits program, including medical, dental, vision, life, disability, and more.
- Paid Times Off and paid holidays
$113.8k - $142.25k
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$244.8k
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$190k - $250k
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