Perception Engineer

Production Computer Vision · NVIDIA Edge Systems

San Francisco, CA | 4 days onsite, 1 day remote
$220,000–$270,000 base + competitive equity | Full-time | Relocation available

Build perception systems that bring advances in autonomous robotics and self-driving technology to residential security.

Our client is an early-stage startup with $18M raised and a team of approximately 25. Its home security platform is already deployed in the field through active beta programs. The team is looking for a hands-on Perception Engineer to take ownership of the perception stack and improve how the system detects, tracks, and understands activity around a home.

What You’ll Own

You’ll work across models, video pipelines, and edge deployment, turning computer vision capabilities into reliable features running on NVIDIA hardware.

• Scale the perception pipeline: Re-architect the existing service on NVIDIA Thor to support more camera streams and improve stability.

• Improve real-time performance: Evaluate and deploy faster, more capable models within edge compute constraints.

• Add depth and tracking capabilities: Bring monocular depth estimation into production to support 3D tracking of people and vehicles.

• Optimize the NVIDIA stack: Work with DeepStream, JetPack, and TensorRT to improve inference and video processing.

• Ship across the system: Partner with device and systems engineers to deliver improvements to deployed hardware.

This is an individual contributor role with substantial technical ownership.

What You Bring

• Approximately 3–7 years of relevant experience, including ownership of production perception or computer vision systems.

• Strong C++ and Python skills and experience writing production software.

• Hands-on experience with NVIDIA DeepStream, JetPack, and TensorRT, ideally deploying on Jetson Thor or Orin.

• Production experience with GStreamer and/or FFmpeg video pipelines.

• Applied computer vision and deep learning experience, including model deployment, PyTorch, multi-object tracking, and multi-camera association.

• Experience making architecture and performance decisions for systems deployed on edge hardware.

• A background in a safety-critical field such as robotics, autonomous vehicles, aerospace, medical devices, or physical security.

• A BS or MS in Computer Science, or equivalent experience.

The strongest candidates can explain what they built, the tradeoffs they made, and how their systems performed after deployment.

Compensation & Benefits

• $220,000–$270,000 base salary, with flexibility for exceptional candidates

• Competitive seed-stage equity

• Relocation assistance

• Health insurance, 401(k), commuter benefits, and fertility benefits

Candidates on OPT or seeking an H-1B transfer may be considered. New visa sponsorship is not available.

Interview Process

1. Hiring manager conversation — 30 minutes, virtual: Your experience, ownership approach, and interest in an early-stage team.

2. Technical conversation — 45 minutes, virtual: A project deep dive and live perception/ML problem-solving, rather than LeetCode-style exercises.

3. Onsite interviews — San Francisco: Four sessions covering perception and ML, device and video pipelines, system design and software engineering, and collaboration and leadership.