Velodyne Lidar provides smart, powerful lidar solutions for autonomy and driver assistance. Headquartered in San Jose, CA, Velodyne is known...
Velodyne Lidar provides smart, powerful lidar solutions for autonomy and driver assistance. Headquartered in San Jose, CA, Velodyne is known worldwide for its portfolio of breakthrough lidar sensor technologies. Our lidar technology has revolutionized perception and autonomy for automotive, new mobility, mapping, robotics, and security applications. Our mass-market adoption has created new opportunities in the software space, and we are currently developing a suite of new services.
Velodyne Lidar is seeking a Staff ML Optimization Engineer for Velodyne Software. The candidate in this role will be responsible for optimizing Deep-Learning based Lidar perception models on different hardware platforms such as mobile GPUs, SOCs and FPGAs. This role will require in-depth combined knowledge of Machine Learning, Computer vision, Computer Architectures and System-Level information of SOCs.
Specific Job Requirements
- Identify the performance bottlenecks in the existing Deep-Learning models on different hardware platforms.
- Collaborate with ML engineers, architects, and product management to define and architect product platform.
- Explore and research new and emerging SOCs, ASICs or FPGAs for higher efficiency of CNN for 3-d data.
Education And Experience Requirements
Preferred Education And Experience Requirements
- BS/MS/PhD in Computer Science, Electrical Engineering, Applied Mathematics or equivalent engineering field with a focus on machine learning, robotics, computer vision and similar fields
- Requires minimum of 8 years with Bachelor's degree; or 6 years and a Master's degree or a PhD with 3 years experience; or equivalent experience
Note to all recruitment agencies: Velodyne Lidar does not accept agency resumes. Please do not forward resumes to our career page or any Velodyne employees. Velodyne is not responsible for any fees related to any unsolicited resumes.
- 3+ years of industry experience in system implementation of low-level hardware such as SOCs, FPGAs or ASICs.
- Strong understanding of parallel computing architectures such as GPUs, FPGAs and SOCs, operating systems, and systems concepts like process management, file systems, memory, network storage etc.
- Hands on experience in Python or C
- Strong team player with proficient verbal and written communication skills.