Machine Learning Engineer II

We are looking for an experienced Machine Learning Engineer with deep expertise in Computer Vision and Generative AI to join our team and own the end-to-end development and improvement of various innovative problem statements. You will take full ownership of projects, from ideation to delivery, ensuring successful deployment as well as ML monitoring post-launch.

What You Will Be Doing

• Own the entire lifecycle of the Vision and GenAI problem statement, from initial concept throughout deployment.
• Research, design, develop, and deploy robust and scalable ML systems for various Vision use-cases.
• Optimize model training and inference pipelines to maximize GPU utilization and minimize costs.
• Collaborate with Product, Backend, and Platform teams to define project timelines, ensure alignment of business goals, and drive strong execution.

What We're Looking For

• Solid foundation in Deep Learning, Computer Vision, and Generative AI, with a proven experience of 3-5 years, preferably in a fast-paced startup environment.
• In-depth and practical knowledge of CNNs, GANs, VAEs, Diffusion models, and Inpainting methods, image processing techniques, text-to-image and image-to-image generation architectures, etc.
• Strong programming skills in Python and proficiency with ML frameworks (e., Tensorflow, Pytorch, JAX) - create code that is understandable, simple, clean, and easily shared with others.
• Experience in deploying Vision models on edge devices, optimizing for resource constraints.
• Knack for staying up to date with the latest research and trying out unconventional, out-of-the- box ideas.
• Passion for problem-solving and creative thinking, with the ability to break down complex and abstract problems into actionable items.
• Self-motivated to build, with an ability to thrive with minimal oversight and process.

Bonus Points:

• Experienced with 3D computer vision, video processing, and text-to-video, image-to-video generation.
• Knowledge of the Rust programming language for implementing the inference pipelines.
• Experienced working with highly skewed and imbalanced data.
• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).