Senior Machine Learning Engineer (Computer Vision)
F
FactoredAI, ML, Data
Location: Latin America Fully remote | Complete engagement jobFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- +5 years of hands-on experience developing and deploying machine learning models in production environments.
- Required Skills
- AWSPythonGCPImage ProcessingKerasMachine LearningOpenCVPyTorchAzureTensorflowDeep LearningMLOpsComputer Vision
Requirements
- +5 years of hands-on experience developing and deploying machine learning models in production environments.
- Proven experience writing production-level code, with strong proficiency in Python.
- Strong Python programming skills with proficiency in deep learning frameworks (TensorFlow, PyTorch, or Keras).
- Expertise in designing, training, and fine-tuning models for: Image classification (ResNet, EfficientNet), Object detection (Faster R-CNN, YOLO, SSD) or Image segmentation (U-Net, Mask R-CNN).
- Strong understanding of image preprocessing techniques (resizing, normalization, data augmentation).
- Experience with computer vision libraries such as OpenCV and torchvision.
- Experience with transfer learning and adapting pre-trained models.
- Ability to deploy models on cloud platforms (AWS, GCP, Azure) and specialized hardware (GPUs, TPUs).
- Familiarity with MLOps tools for automating ML pipelines.
Responsibilities
- Design and deliver advanced vision systems that power mission-critical applications for global and Fortune 500 companies.
- Work across deep learning, large-scale data pipelines, and high-performance infrastructure, owning models end-to-end from experimentation to production deployment.
- Shape architectures, guide model strategy, and bring modern vision capabilities into enterprise environments where reliability, speed, and accuracy matter.
- Develop and fine-tune models for tasks like image classification, object detection, segmentation, and generative modeling using TensorFlow, PyTorch, or Keras.
- Implement techniques such as resizing, normalization, data augmentation, and feature extraction to improve model performance.
- Optimize and deploy computer vision models on cloud platforms (AWS, GCP, Azure), edge devices, and specialized hardware (GPUs, TPUs).
- Use CI/CD, model versioning, and monitoring tools to ensure reliable and scalable deployment of vision models.
- Improve model speed and performance using quantization, pruning, and hardware acceleration techniques.
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