Latin America Fully remote | Full engagement job Founded in Palo Alto by Dr. Andrew Ng and Israel Niezen, Factored helps U.S. companies build and scale world‑class AI, ML, and Data teams, powered by the top 1% of LATAM talent, with a defining purpose: To empower brilliant humans, unleash their potential, and amplify their impact in the world. As a Senior Machine Learning Engineer in Computer Vision , you will design and deliver advanced vision systems that power mission‑critical applications for global and Fortune 500 companies. You’ll work across deep learning, large‑scale data pipelines, and high‑performance infrastructure, owning models end‑to‑end from experimentation to production deployment. Responsibilities Develop and fine‑tune models for 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. Qualifications +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 and 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. Benefits Ownership through equity participation. Annual company retreat. Education bonus for continuous learning. Company‑wide winter break. Paid time off. Optional in‑person events and meetups. Tailored career roadmaps. High‑performance culture. #J-18808-Ljbffr