Job Description

General Summary:

At Qualcomm, we are transforming the automotive industry with our Snapdragon Digital Chassis and building the next generation software defined vehicle (SDV). Snapdragon Ride is an integral pillar of our Snapdragon Digital Chassis, and since its launch it has gained momentum with a growing number of global automakers and Tier1 suppliers.  Snapdragon Ride aims to address the complexity of autonomous driving and ADAS by leveraging its high-performance, power-efficient SoC, industry-leading artificial intelligence (AI) technologies, and pioneering vision and drive policy stack to deliver a comprehensive, cost and energy efficient systems solution.

Enabling safe, comfortable, and affordable autonomous driving includes solving some of the most demanding and challenging technological problems. From centimeter-level localization to multimodal sensor perception, sensor fusion, behavior prediction, decision-making, trajectory planning, and control, each one of these functions introduces its own unique challenges to solve, verify, test, and deploy on the road.

We are looking for smart, innovative, and motivated individuals to join our behavior planning team. Applicants should have a strong theoretical background in deep reinforcement learning, experience in probabilistic planning algorithms, and good software implementation skills in Python and C++.

Minimum Qualifications:

Bachelor's degree in Computer Science, Electrical Engineering, Mechanical Engineering, or related field.

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).
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.

Job responsibilities include:

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.