Founding Engineer, Computer Vision & Robotics
NVIDIA Ltd.
United States
7 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
3 years minimum
Compensation
$120,000.0 - $200,000.0
Working hours
Regular working hours
Job source
Tech stack
3D Scanning
Nvidia CUDA
Python (Programming Language)
OpenCV
Pytorch
Low Latency
TensorRT
Job description
Join an early-stage industrial automation team as its first engineer, building computer vision systems that understand manual work in factory settings. You will take perception from experimentation through reliable production deployment, starting with real-time quality inspection.
Requirements
- At least 3 years of experience in computer vision, robotics perception, or applied machine learning, including deploying perception systems beyond notebooks or lab environments.
- Strong Python, PyTorch, and OpenCV skills, with experience building detection, classification, or tracking models.
- Experience shipping real-time perception systems that work reliably in challenging physical environments.
- Background in geometric vision, such as calibration, localization, pose estimation, or coordinate transformations.
- Experience deploying to edge devices or optimizing production inference for low latency.
- Comfort working directly in field settings and using real-world failures to improve models and data.
- Experience with synthetic data and Sim2Real, SLAM or VIO, depth estimation, 3D reconstruction, CUDA or TensorRT, ROS2, robot manipulation, or robot learning is beneficial.
Benefits & conditions
- Build perception capabilities for detection, classification, tracking, temporal understanding, and 3D vision.
- Develop first-person vision systems that handle camera motion, changing viewpoints, occlusion, motion blur, and varied lighting.
- Work with the technical leadership team to turn field failures into improved models, data, and evaluation.
- Build geometric vision systems for calibration, localization, pose estimation, and coordinate transformations.
- Train and debug PyTorch models using synthetic and real-world data.
- Optimize low-latency inference and deploy systems on NVIDIA Jetson with TensorRT.
- Deploy to factory floors and develop reusable perception infrastructure from real-world experience.
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