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Simbe Robotics

Autonomous retail robots for inventory management

Senior Computer Vision / Applied AI Engineer

Computer VisionConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA$160k - $200k /yr

About the Job

Requirements

  • At least 5 years of experience in computer vision, applied machine learning, robotics perception, or related production artificial intelligence systems.
  • Strong Python experience and hands-on experience with PyTorch or TensorFlow.
  • Demonstrated experience using artificial intelligence-assisted coding and automation tools to accelerate software development, debugging, experimentation, and engineering workflows, with sound judgment when validating and reviewing generated output.
  • Experience training and evaluating object detection, segmentation, optical character recognition, visual search, or image recognition models, along with strong knowledge of dataset curation, annotation quality, model evaluation, error analysis, and experimentation.
  • Experience building maintainable production code and data pipelines in a Linux-based environment.
  • Ability to balance research quality with production constraints such as latency, compute, memory, robustness, and ease of deployment.
  • Strong communication skills and a customer-value mindset.

Responsibilities

  • Design, train, validate, and deploy production computer vision models for object detection, segmentation, optical character recognition, barcode localization, product recognition, shelf understanding, and other customer-facing computer vision tasks.
  • Curate, clean, version, and analyze large real-world training datasets, including hard-negative mining, annotation quality assurance, data audits, and active-learning workflows.
  • Research and implement improvements to model architecture, loss functions, data augmentation, synthetic data, and evaluation to increase precision, recall, latency, and robustness across customers.
  • Contribute to model validation, release gates, inference wrappers, ONNX and TensorRT exports, and production monitoring.
  • Build internal tools for model evaluation, annotation review, error mining, data visualization, dataset exports, and deployment readiness.
  • Work closely with Product, Customer Success, Data, Robotics Software, and Field Operations to turn customer issues into model and pipeline improvements.
  • Track modern work in open-vocabulary detection, promptable segmentation, multimodal product understanding, visual search, optical character recognition, and edge inference, and evaluate opportunities to create value.

Desired Qualifications

  • Experience with retail, product recognition, shelf intelligence, optical character recognition, barcode decoding, fine-grained recognition, or visual product search.
  • Experience with ONNX, TensorRT, CUDA, quantization, model profiling, or edge deployment.
  • Experience with C++, ROS or ROS2, RGB-D cameras, 3D geometry, homography, calibration, or robotic perception.
  • Experience with FiftyOne, CVAT, Labelbox, Roboflow, or other data-centric machine-learning tools.
  • Experience with synthetic data generation, simulation, active learning, or automated annotation workflows.
  • Publications, patents, open-source contributions, or production systems in relevant computer vision or artificial-intelligence domains.

About the Company

Simbe Robotics

Autonomous retail robots for inventory management

Simbe Robotics builds autonomous robots for retail stores to improve inventory management and store operations. Its flagship robot, Tally, uses sensors to move through store aisles, detect out-of-stock items, and verify that products are correctly shelved and priced. While collecting data across the store, Simbe processes this information in a backend system to produce actionable insights that help retailers cut losses, optimize shelf space, and boost customer satisfaction. The company generates revenue by selling or leasing robots and offering data analytics services to retailers. What sets Simbe apart is its end-to-end, in-store automation plus analytics tailored to retail needs, combining physical robot performance with data-driven recommendations to reduce manual labor and improve store efficiency. The main goal is to help retailers operate more efficiently, cut costs, and provide better shopping experiences for customers.

Company size
51-200
Company stage
Series C
Total funding
$104M
Headquarters
South San Francisco, CA
Founded
2014

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