AI Planner – ADAS R&D Systems
| Company | Qualcomm |
|---|---|
| Location | San Diego, CA, USA |
| Salary | $179000 – $268400 |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s, PhD |
| Experience Level | Senior, Expert or higher |
Requirements
- Bachelor’s degree in Computer Science, Electrical Engineering, Mechanical Engineering, or related field and 6+ years of Systems Engineering or related work experience.
- Master’s degree in Computer Science, Electrical Engineering, Mechanical Engineering, or related field and 5+ years of Systems Engineering or related work experience.
- PhD in Computer Science, Electrical Engineering, Mechanical Engineering, or related field and 4+ years of Systems Engineering or related work experience.
Responsibilities
- Applies knowledge of ADAS Systems to develop and enhance technologies for autonomous driving.
- Researches and develops novel algorithms to solve difficult problems related to behavior planning under uncertainty.
- Designs driving policies for complex maneuvers in urban environments with many other agents.
- Implements ideas in software (Python and C++) and collaborates with software engineers on development.
- Works closely with prediction and motion planning teams to define interfaces, requirements, and KPIs.
- Works closely with test engineers to develop test plans to validate performance in simulations and real-world testing.
- Provides support for patentable ideas and assists with their implementation.
- Networks with cross-functional teams and leverages other resources to acquire knowledge of ADAS industry trends, and advances in the field.
- Writes detailed technical documentation and descriptions of research findings for projects to ensure engineers can implement research.
Preferred Qualifications
- Ph.D. + 2 years industry experience in behavior planning.
- 3+ years of experience with Programming Language such as C, C++, Python, etc.
- Expert knowledge and experience learning-based planning approaches like deep reinforcement learning, imitation learning and vector/point-based input representations for learning.
- Programming experience implementing cutting-edge deep learned ML solutions using PyTorch/Tensorflow and training.
- Strong understanding of ML workflows: data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, and inference optimization (bonus).
- Experience with offline RL techniques.
- Analytical and scientific mindset, with the ability to solve complex problems. Experience with robust software design for safety-critical systems.
Benefits
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No information provided on Benefits.