Perception Software Engineer

### Who you are
- Team members may work remotely and must be self-motivated
- Expertise with perception algorithms as shown with academic and/or professional experience
- Proficient at translating perception algorithms into production quality C++ (C++14 or newer) code
- Strong debugging skills across distributed and asynchronous pipelines, using logs and metrics to drive root-cause analysis
- Excellent verbal and written communication skills

### What the job involves
- The Glydways perception team is responsible for designing and implementing a perception system that includes data fusion from state-of-the-art sensors (e.g., LIDAR, RADAR, High definition cameras, Ultra-wide-band radios, etc.) and robust detection, classification, and tracking of any and all obstacles that could present a hazard to the vehicle (Glydcars) in the system
- The perception system for Glydways will reason about information from each Glydcar and from regularly spaced sensor pods monitoring the road network
- The perception team plays a vital role within the Autonomy Software engineering team. The team’s deliverables include:
- Documenting a detailed design for enacting a safe system that can be certified by public transportation authorities
- Implementing said design on mature prototype vehicles in demonstrations to customers, potential customers, and investors
- Expanding the design to include fail-operational capabilities that extend the overall system to safely give our customers a more comfortable experience
- Managing data collection, autonomy testing, and milestone demonstration events that showcase the system’s maturing capabilities, leading to a production system
- Implement and own perception algorithms spanning detection, classification, sensor fusion, and multi-object tracking using both ML-based and classical methods
- Develop end-to-end perception pipelines from raw sensor input (camera, LiDAR, radar) to structured scene understanding outputs consumed by downstream autonomy modules
- Develop and maintain simulation, log-replay, visualization, and offline analysis workflows to maximize test coverage and accelerate validation prior to on-vehicle deployment
- Build tooling and infrastructure for dataset management, model training, evaluation, and deployment at scale