Machine Learning Engineer (Computer Vision)

Factored was conceived in Palo Alto, California by Andrew Ng and a team of highly experienced AI researchers, educators, and engineers to help address the significant shortage of qualified AI & Machine-Learning engineers globally. ​We know that exceptional technical aptitude, intelligence, communication skills, and passion are equally distributed around the world, and we are very committed to testing, vetting, and nurturing the most talented engineers for our program and on behalf of our clients.

We are seeking a highly skilled Machine Learning Engineer with expertise in computer vision to design, develop, and optimize deep learning models for image-related tasks. The ideal candidate will have strong experience in building and deploying models for image classification, object detection, and image segmentation. This role offers the opportunity to work on cutting-edge AI solutions while collaborating with a team of researchers and engineers.

 

Functional Responsibilities:

• 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.

Qualifications:

• 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)
  • 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.

 

At Factored, we believe that passionate, smart people expect honesty and transparency, as well as the freedom to do the best work of their lives while learning and growing as much as possible. Great people enjoy working with other passionate, smart people, so we believe in hiring right, and are very selective about who joins our team. Once we hire you, we will invest in you and support your career and professional growth in many meaningful ways. We hire people who are supremely intelligent and talented, but we recognize that intelligence is not enough. Perhaps more importantly, we look for those who are also passionate about our mission and are honest, diligent, collaborative, kind to others, and fun to be around. Life is too short to work with people who don’t inspire you.     We are a transparent workplace, where EVERYBODY has a voice in building OUR company, and where learning and growth are available to everyone based on their merits, not just on stamps on their resume. As impressive as some of the stamps on our resumes are, we recognize that human talent and passion exist everywhere, and come from many backgrounds, so stamps matter much less than results. All of us are dedicated doers and are highly energetic, focusing vehemently on execution because we know that the best learning happens by doing. We recognize that we are creating OUR COMPANY TOGETHER, which is not only a high-performing fast-growing business but is changing the way the world perceives the quality of technical talent in Latin America. We are fueled by the great positive impact we are making in the places where we do business and are committed to accelerating careers and investing in hundreds (and hopefully thousands) of highly talented data science engineers and data analysts.    In short, our business is about people, so we hire the best people and invest as much as possible in making them fall in love with their work, their learning, and their mission.  When not nerding out on data science, we love to make music together, play sports, play games, dance salsa, cook delicious food, brew the best coffee, throw the best parties, and generally have a great time with each other.