Designing end2end solutions for Perception and AV stack to enable road network detections across various driving environments from complex intersections to rural curvy roads to multi-level highways.
Applied research and development of innovative deep learning models for lane graph construction, road boundary detection, traffic element recognition, and other static-world tasks.
Develop generalizable approaches to support diverse ODDs and Country/region expansion
Drive and prioritize data-driven development by working with large data collection and labeling teams to bring in high value data to improve perception system accuracy.
Productize the developed perception solutions by meeting product requirements for safety, latency, and SW robustness.

Requirements

PhD with 4+ years, MS with 6+ years, or BS with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.
2+ years of technical leadership demonstrating high technical and organizational complexity is a big plus.
Hands-on work experience in developing deep learning and algorithms to solve sophisticated real world problems, and proficiency in using deep learning frameworks (e.g., PyTorch).
Experience in data-driven development and collaboration with data and ground truth teams.
Strong programming skills in python and/or C++.

Benefits

equity
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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

deep learningalgorithmslane graph constructionroad boundary detectiontraffic element recognitiondata-driven developmentprogramming in Pythonprogramming in C++technical leadershipproduct requirements

Soft Skills

organizational complexitycollaborationproblem-solvingprioritization

Certifications

PhD in Computer ScienceMS in Computer ScienceBS in Computer Science