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Chooch AI
Chooch AI
51 – 200 Employees
HealthcareLogisticsManufacturing
Chooch AI develops computer vision software that turns images and video into real-time operational insights and automated actions. Its platform supports hospital inventory management, remote patient monitoring for falls and distress, and facility safety monitoring. It connects with existing ERP and safety systems to automate alerts, replenishment, and related workflows, and is delivered through APIs, edge-device deployments, and a SaaS platform for healthcare and facility operators. The company’s work spans healthcare, logistics, and manufacturing.
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Machine Learning Engineer (Hybrid)

Build and deploy computer vision models for Chooch AI’s intelligent camera platform. Support customer deployments across healthcare, manufacturing, and retail.

USUnited StatesCACalifornia
$120,000 – $180,000 / year
HybridFull-timeMiddleSeniorEnglish RequiredMachine Learning Engineer
AWSAzureCloudDockerLinuxPythonPyTorchTensorflow

Description

  • Design, train, and refine computer vision models for customer video and image data.
  • Adapt pretrained and foundation vision models to new customer use cases, including cases with limited labeled data.
  • Manage data pipelines from curation and labeling through augmentation, synthetic data, and dataset versioning.
  • Prepare models for production and edge devices using techniques such as quantization, pruning, distillation, ONNX export, and TensorRT.
  • Create evaluation suites using measures such as mAP, precision and recall, and false alarms per camera-hour.
  • Deploy and monitor production models, investigate drift and failures, and retrain as needed.
  • Translate requirements into measurable model specifications with product teams, solutions engineers, and customers.
  • Write, test, and review code, and contribute to shared machine learning tools.
  • Help collect, process, and maintain data for model training and testing.
  • Connect machine learning models with wider products and systems.
  • Travel to customer sites and conduct field tests for live deployments when needed.
  • Assess model performance with qualitative and quantitative methods, then improve accuracy and efficiency.
  • Track and apply developments in machine learning and computer vision.
  • Resolve complex issues in computer vision system development and deployment.

Requirements

  • Bring 3–5 years of industry experience building machine learning systems, including at least 2 years focused on computer vision.
  • Hold a BS or MS in Computer Science, Electrical Engineering, Math, or a related field, or have equivalent practical experience.
  • Have strong Python skills and practical experience training and debugging deep learning models with PyTorch or TensorFlow.
  • Have deployed at least one computer vision model to production and be able to explain the trade-offs involved.
  • Understand CNN and transformer architectures for detection, segmentation, and classification.
  • Have experience with OpenCV and image or video processing, including stream decoding, preprocessing, and augmentation.
  • Know model inference optimization methods such as ONNX, TensorRT, or quantization.
  • Be comfortable with Docker, Git, Linux, and GPU training in a cloud environment such as AWS or Azure.
  • Understand machine learning fundamentals, including loss functions, overfitting, class imbalance, evaluation metrics, and experiment design.
  • Communicate clearly in writing and conversation, including explaining model behavior to non-ML stakeholders.

Benefits

  • Health insurance coverage.
  • Dental insurance coverage.
  • Vision insurance coverage.
  • 401(k) plan with a 3% match.
  • Extensive paid time off.
  • Life insurance policies included.

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