Machine Learning Engineer - Computer Vision
TypeFull-time job
LocationUnited States
Posted1 hour ago
We're hiring a Machine Learning Engineer to join the team of one of our clients, a computer vision AI company whose platform turns standard cameras into intelligent systems that raise productivity and reduce operating costs for enterprise customers in healthcare, manufacturing, and retail.
You will be responsible for the design, accuracy, and backbone of the client's computer vision applications: preparing data, training and configuring neural networks, deploying models in real customer environments, and refining algorithms. The role involves regular interaction with technical and non-technical stakeholders and close collaboration with the solutions engineering team, with room to grow, lead technical projects, and improve current processes.
The Product
The platform helps hospitals automate supply room monitoring and inventory management. Fixed cameras and AI continuously track supplies in storage areas and update on-hand counts in real time. When inventory falls below defined thresholds, replenishment orders are generated automatically and sent to ERP systems, eliminating manual counts and barcode scanning. Hospitals reduce critical stockouts, optimize inventory levels, and free clinical and supply chain teams to spend more time on patient care.
What You Will Do
Design, train, and fine-tune models for object detection, classification, segmentation, tracking, and anomaly detection on real customer video and images.
Adapt pretrained and foundation models (YOLO-family detectors, ViTs, CLIP-style and vision-language models) to new use cases with limited labeled data.
Own data pipelines end to end: curation, labeling guidelines, augmentation, synthetic data, and dataset versioning.
Optimize models for production and edge hardware using quantization, pruning, distillation, ONNX export, and TensorRT, meeting agreed latency and accuracy targets.
Build evaluation suites with clear metrics (mAP, precision/recall, false-alarm rates per camera-hour) and catch regressions before release.
Deploy and monitor models in production, diagnose drift and failure cases, and close the loop with retraining.
Write clean, tested, reviewed code and contribute to shared ML tooling.
Requirements
Solid experience building ML systems, with at least 2 years focused on computer vision.
Strong Python and hands-on experience with PyTorch (or TensorFlow) for training and debugging deep learning models.
At least one computer vision model shipped to production, with the ability to explain the trade-offs made along the way.
Solid grasp of CNN and transformer architectures for detection, segmentation, and classification.
Experience with OpenCV and image/video processing: decoding streams, preprocessing, and augmentation.
Working knowledge of model optimization for inference (ONNX, TensorRT, quantization, or similar).
Comfortable with Docker, Git, Linux, and training on GPUs in a cloud environment (AWS or Azure).
Sound ML fundamentals: loss functions, overfitting, class imbalance, evaluation metrics, and experiment design.
Clear written and verbal communication in English.
Why join us:
Remote job from LATAM
USD compensation
Paid time off
Long-term engagements
Collaborate with international teams on innovative, real-world AI projects.
Originally posted on Himalayas
You will be responsible for the design, accuracy, and backbone of the client's computer vision applications: preparing data, training and configuring neural networks, deploying models in real customer environments, and refining algorithms. The role involves regular interaction with technical and non-technical stakeholders and close collaboration with the solutions engineering team, with room to grow, lead technical projects, and improve current processes.
The Product
The platform helps hospitals automate supply room monitoring and inventory management. Fixed cameras and AI continuously track supplies in storage areas and update on-hand counts in real time. When inventory falls below defined thresholds, replenishment orders are generated automatically and sent to ERP systems, eliminating manual counts and barcode scanning. Hospitals reduce critical stockouts, optimize inventory levels, and free clinical and supply chain teams to spend more time on patient care.
What You Will Do
Design, train, and fine-tune models for object detection, classification, segmentation, tracking, and anomaly detection on real customer video and images.
Adapt pretrained and foundation models (YOLO-family detectors, ViTs, CLIP-style and vision-language models) to new use cases with limited labeled data.
Own data pipelines end to end: curation, labeling guidelines, augmentation, synthetic data, and dataset versioning.
Optimize models for production and edge hardware using quantization, pruning, distillation, ONNX export, and TensorRT, meeting agreed latency and accuracy targets.
Build evaluation suites with clear metrics (mAP, precision/recall, false-alarm rates per camera-hour) and catch regressions before release.
Deploy and monitor models in production, diagnose drift and failure cases, and close the loop with retraining.
Write clean, tested, reviewed code and contribute to shared ML tooling.
Requirements
Solid experience building ML systems, with at least 2 years focused on computer vision.
Strong Python and hands-on experience with PyTorch (or TensorFlow) for training and debugging deep learning models.
At least one computer vision model shipped to production, with the ability to explain the trade-offs made along the way.
Solid grasp of CNN and transformer architectures for detection, segmentation, and classification.
Experience with OpenCV and image/video processing: decoding streams, preprocessing, and augmentation.
Working knowledge of model optimization for inference (ONNX, TensorRT, quantization, or similar).
Comfortable with Docker, Git, Linux, and training on GPUs in a cloud environment (AWS or Azure).
Sound ML fundamentals: loss functions, overfitting, class imbalance, evaluation metrics, and experiment design.
Clear written and verbal communication in English.
Why join us:
Remote job from LATAM
USD compensation
Paid time off
Long-term engagements
Collaborate with international teams on innovative, real-world AI projects.
Originally posted on Himalayas
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