Perception Engineer

About the company

Our client is a technology company.

The role

Raydar is recruiting for this role on behalf of our client. Own and improve a production computer vision service running on embedded hardware, delivering performance and stability gains to a system already in use in the field. The work centers on tuning real-time video and model pipelines and partnering closely with hardware and systems colleagues.

What you'll do

• Re-architect and optimize an existing real-time perception service so it handles more video streams with better stability.
• Evaluate and integrate faster, higher-performing models while keeping real-time performance on edge hardware.
• Bring depth estimation capabilities onto embedded compute to support three-dimensional tracking.
• Tune the video and inference stack on edge devices to get the most out of the hardware.
• Work with hardware and systems engineers to ship perception improvements to deployed systems.

Requirements

What we're looking for

• 4 to 7 years of experience, with strong Python and C++ skills.
• Hands-on experience building production perception or machine learning systems, beyond research work.
• Familiarity with GPU-accelerated inference tooling and deployment on embedded edge devices.
• Prior work applying computer vision within physical, hardware-based products.
• Solid grasp of video pipelines and how camera feeds behave in real deployments.
• Track record of taking vision models from development into live use, including object tracking across frames.
• Ability to own system architecture at a senior level.
• Self-directed working style with comfort collaborating across functions in a fast-moving startup.
• Bachelor's or master's degree in computer science.

Bonus points

• Exposure to industries where reliability and safety are critical.

Benefits

Compensation and benefits

• Base salary: USD 220,000 to 270,000 per year
• Equity
• Health insurance
• Retirement plan
• Commuter benefits
• Family planning benefits

Location and work model

• San Francisco, CA, United States
• Hybrid, 4 days in office
• Full-time