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Closed listing

True Talent Solutions Partners

Computer Vision ML / Research Engineer

San Francisco, CA, United States

$180,000 to $245,000 USD / year

This opening has closed.

The original listing is kept below for reference.

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Job details

Pay: $180,000 to $245,000 USD / year
Posting language: English
Posted: Nov 2, 2026

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Original job description

Archived listing. The details below describe a past opening.

ABOUT THE ROLE

Join an industrial computer vision team building a perception system that turns stereo camera images into accurate, auditable inventory measurements. You will own the core vision and machine learning pipeline, helping bring research methods into reliable production use.

WHAT YOU'LL DO

Build and improve stereo reconstruction, depth estimation, segmentation, and 3D volume measurement workflows.
Integrate computer vision models and develop point-cloud cleanup, ground-plane estimation, and scene-stitching methods.
Evaluate reconstruction quality and volume accuracy, and investigate failure modes using real-world sensor data.
Define calibration assumptions and develop confidence metrics for production-grade measurements.
Move research pipelines into robust systems that support industrial inventory measurement.

WHAT WE'RE LOOKING FOR

Four or more years of experience in computer vision or perception machine learning engineering.
Strong Python and PyTorch skills, with experience using OpenCV and Open3D.
Hands-on experience with stereo vision, depth estimation, point clouds, 3D reconstruction, and 3D geometry.
Experience shipping computer vision systems that use real-world sensor data, including camera calibration and volume reconstruction.
Background in robotics perception, autonomous systems, industrial inspection, photogrammetry, or related areas is valuable.
CUDA, SLAM, YOLO-style models, FoundationStereo, or GPU cloud experience is a plus.
A relevant master's or doctoral degree is beneficial.

COMPENSATION & BENEFITS

Base salary: $180,000 to $245,000 annually. Visa sponsorship is available.

LOCATION

On-site in San Francisco, California, United States.

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