Real-Time Robotic Perception Lead

Kovari

San Francisco (CA)

On-site

USD 150,000 - 230,000

Full time

14 days+

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Job summary

Kovari is seeking a senior robotics perception engineer to own the perception stack from sensors to actionable representations for both learned policies and classical control. You will work on deployed robots in real hotel environments, addressing variable lighting, glass, obstacles, and repetitive architecture.

You will build perception systems for manipulation and localization, optimize camera pipelines for low latency, and develop real-time detection, segmentation, and pose estimation for

Qualifications

  • Expertise in developing low-latency camera pipelines for real-time perception.
  • Strong background in classical computer vision and 3D sensing.
  • Experience with learned perception methods and state estimation fusion.
  • Proven track record building safety-critical perception on deployed robotics.

Responsibilities

  • Build perception systems for manipulation tasks and localization/navigation.
  • Optimize camera pipelines for low latency and high-frequency performance.
  • Develop real-time object detection, segmentation, and pose estimation.
  • Address environment-specific challenges: dynamic obstacles, lighting variation, reflective surfaces.
  • Debug perception failure modes on deployed hardware in the field.

Skills

Deep C++ & systems
Classical CV: calibration, SLAM, VO
Learned perception: detection, seg., 3
Sensor fusion & state estimation
Safety-critical robotics on deployed h

Job description

Kovari is seeking a senior robotics perception engineer to own the perception stack from sensors to actionable representations for both learned policies and classical control. You will work on deployed robots in real hotel environments, addressing variable lighting, glass, obstacles, and repetitive architecture.

You will build perception systems for manipulation and localization, optimize camera pipelines for low latency, and develop real-time detection, segmentation, and pose estimation for

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