We're seeking a skilled Machine Learning Optimization Engineer to enhance our ADAS/Autonomous Driving division. This role demands strong programming skills and a deep understanding of modern tools for optimizing Machine Learning and deep learning models, specifically for efficient inference.
This role is located on-site at Headquarters in Newark, CA.
Role and Responsibilities
- Collaborate with diverse engineering teams to analyze AD hardware and identify optimization opportunities for deep learning models, understanding perception model architectures, and deploying models on various target platforms.
- Lead the technical roadmap for DL model optimization across current and future AD target hardware, including repetitive engineering tasks such as researching, prototyping, and implementing optimization techniques like quantization, compression, and pruning.
- Develop and implement custom optimizations for models using internal datasets and benchmarks, and seamlessly integrate these optimizations into existing model training pipelines and workflows, requiring meticulous attention to detail and a methodical approach.
- Conduct unit tests to ensure the reliability and accuracy of implemented optimizations, emphasizing the iterative nature of the optimization process.
- Debug and modify codebases, particularly those within the deployment pipeline for deep learning models, and optimize preprocessing/postprocessing code for target devices using CUDA kernels to minimize latency, necessitating patience and persistence.
- Utilize strong proficiency in Python and C++ programming, with a focus on software engineering principles, and demonstrate excellent problem-solving skills and a passion for technology.
Required Qualifications:
- Bachelor degree in the one of the following; computer science, Machine Learning, computer engineer
- 5+ years of related experience.
- Experience in writing CUDA kernels and developing TensorRT plugins, showcasing a willingness to delve into low-level optimizations.
- Proficiency in C/C++ programming, especially for embedded devices, underscoring the practical engineering aspect of the role.
- Knowledge of relevant safety standards and regulations (ASPICE, ISO 26262), highlighting the attention to compliance required in automotive engineering.
- Bachelor's degree in Computer Engineering, Electrical Engineering, Automotive Engineering, Mechanical Engineering, or related fields.
Preferred Qualifications:
- Masters degree in the one of the following; computer science, Machine Learning, computer engineer
- 3 years of professional experience or a Ph.D with no experience.
- Knowledge of common automotive sensors (e.g. Camera, Radar, Lidar, etc.)
- Experience in working in agile development teams
- Experience in component and system integration, testing and verification on system and vehicle level
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