Overview
In this role, you will advance perception and 3D understanding for driver assistance, working across data, training, evaluation, and iteration to ship robust models. You’ll balance on-car and offline work, building scalable data pipelines and automatic labeling to accelerate development. You’ll partner with the ADAS team to improve real-world driving performance through better perception. This opportunity offers ownership in a mission-driven company building a mapless, scalable AI platform for autonomy.
Pay / Benefits
- salary benchmarking
- meaningful equity
- relocation support and visa sponsorship
- hybrid working and core hours
- learning and development budgets
- comprehensive health and wellbeing benefits
Responsibilities
- Train, debug, and improve CV and 3D perception models using clear evaluation signals
- Own the end-to-end ML lifecycle: data, training, evaluation, and iteration
- Collaborate to decide next priorities based on system underperformance
- Build scalable data pipelines, including auto-labeling and pseudo-labeling
- Develop core ADAS perception capabilities (detection, classification, instance segmentation) across lanes, objects, signs, and signals
- Pursue end-to-end approaches enabling the model to act directly in the world
- Support online perception for in-car production systems and offline perception for data quality and coverage at scale
- Create a data flywheel by using offline models and 3D tracking/reconstruction to auto-generate labels
Key requirements
- Built and shipped CV-focused deep learning systems
- Experience with 3D perception concepts or pipelines (LiDAR, multi-view geometry, tracking, 3D reconstruction)
- Comfortable owning work end-to-end, including evaluation and large-scale data generation
- Pragmatic problem-solving under real product constraints
- Evidence of interest in improving real-world driving perception
- Automotive domain experience is a bonus
- pragmatic problem-solving
- ownership of end-to-end work
- ability to collaborate across teams
- CV-focused deep learning systems
- 3D perception (LiDAR, tracking, 3D reconstruction)
- data pipelines and data generation
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