What You'll Do

• Own ML pipelines end-to-end—from prototyping to deployment to measuring customer impact
• Focus relentlessly on delivering customer value
• Collaborate across R&D to deliver full-scope solutions, not just ML components
• Rapidly prototype and prioritize algorithms based on user needs
• Build and scale ML systems using state-of-the-art techniques
• Lead efforts to acquire and manage high-quality datasets

What You'll Need To Be Successful

• Experience in writing production code with a focus on maintainability and performance.
• Experience training deep learning models, including expertise in model selection, training, optimization, and deployment.
• You have startup DNA: a growth mindset, a bias toward action, and a drive to take ownership of challenging projects with minimal guidance. You’re resourceful—when you hit a wall, you find a way around it, learn what you need, and keep moving forward.
• Computer vision expertise (or a strong foundation in deep learning from other domains, such as NLP). If you don’t have direct computer vision experience, a willingness to apply your deep learning knowledge to this area is important.
• Nice to have: Experience with cloud infrastructure, such as AWS, GCP, or Azure, and familiarity with scaling machine learning models in a cloud-based environment.