Drive the development of ML-powered features from early-stage research prototypes to robust production deployments.
Compose, build, and optimize scalable ML pipelines for video understanding, multimodal intelligence, and generative AI applications.
Implement, experiment with, and fine-tune brand new deep learning models across Computer Vision, Video AI, and Generative AI domains.
Solve real-world creator challenges involving spatial, temporal, and multimodal data.
Lead model evaluation, benchmarking, validation, and performance optimization to ensure high quality and reliability.
Optimize and deploy models across heterogeneous environments (CPU/GPU/NPU), including ONNX and CoreML workflows.
Improve model efficiency through quantization, pruning, distillation, and inference acceleration techniques.
Write clean, modular, maintainable, and well-tested production-quality ML code.
Collaborate cross-functionally with researchers, product managers, designers, and platform engineers to deliver impactful roadmap-aligned features.
Remain updated on emerging ML techniques and actively integrate innovations into video and media workflows.

5+ years of hands-on experience in Machine Learning, Deep Learning, or related domains.
Proven experience building, scaling, and deploying ML models in production environments with measurable impact.
Strong proficiency in PyTorch (preferred) or TensorFlow, and Python-based ML development.
Solid foundations in mathematical modeling, including Linear Algebra, Probability, Statistics, and optimization theory.
Deep understanding of core ML principles: model training, validation, generalization, and performance trade-offs.
Strong grasp of deep learning architectures including CNNs, RNNs, Transformers, and multimodal models.
Practical experience in computer vision tasks such as object detection, segmentation, tracking, video understanding, or temporal modeling.
Familiarity with model optimization and deployment techniques including pre/post training quantization, ONNX, CoreML, and edge deployment.
Understanding of software engineering guidelines including version control (Git), testing frameworks, CI/CD, and code review processes.
Strong analytical thinking, problem-solving ability, and curiosity to learn in a fast-evolving AI landscape.
Excellent communication skills and the ability to collaborate effectively within diverse, high-performing teams.

Machine Learning Engineer

Key skills