Staff SW Quality Architect: Planner for Autonomous Driving

Waabi; United States; Hybrid; USD 245,000 - 285,000; Full time

Benefits offered by this job

Competitive compensation and equity

Health and Wellness benefits

Unlimited vacation

Flexible hours and Work from Home

Catering and team events

Job summary

Waabi is hiring to lead the Software Quality of the Planner Architecture, focusing on fault-tolerance and reliability to ensure safe vehicle operation. You will bridge Motion Planning and Onboard SW teams, driving overall software quality improvements and coordinating the development of motion planning algorithms.

Join a multidisciplinary team using an AI-first approach to enable safe self-driving at scale, while managing the experimentation pipeline for research and data generation.

Qualifications

• MS/PhD or Bachelor degree with a minimum of 6 years of industry experience in CS/Robotics or similar fields.
• Experience owning software on a shipped product with reliability requirements.
• Production-quality coding in Python or C++.

Responsibilities

• Lead the Software Quality of the Planner Architecture, building fault-tolerance and reliability.
• Serve as a technical bridge between Motion Planning and Onboard SW teams to improve SW quality.
• Design and develop motion planning software and algorithms for dynamic, uncertain traffic scenarios.
• Be part of a multidisciplinary team using AI-first approaches for safe self-driving at scale.
• Manage the experimentation pipeline for research and data generation.

Skills

Python

C++

Software engineering

Education

MS/PhD or Bachelor's with 6 years experience

Job description

Waabi is hiring to lead the Software Quality of the Planner Architecture, focusing on fault-tolerance and reliability to ensure safe vehicle operation. You will bridge Motion Planning and Onboard SW teams, driving overall software quality improvements and coordinating the development of motion planning algorithms.

Join a multidisciplinary team using an AI-first approach to enable safe self-driving at scale, while managing the experimentation pipeline for research and data generation.