We are seeking a passionate, talented, and inventive scientist to join the Amazon AGI team. As an Applied Scientist, you'll be at the forefront of developing intelligent systems that can seamlessly process, understand, and retrieve multimodal information.
We're seeking a creative problem-solver who's excited about architecting novel deep learning solutions for multimodal search and retrieval. You'll work on advancing the state-of-the-art in vision-language models and multimodal embeddings, while developing efficient and scalable algorithms for cross-modal retrieval. Your role will involve creating innovative solutions for multimodal ranking and relevance, ultimately building the next generation of multimodal search systems that can understand and process information the way humans do.
You'll collaborate with a talented team of researchers and engineers, contribute to research in multimodal search, and see your innovations directly impact millions of customers. If you're passionate about multimodal search and want to shape the future of how machines understand and retrieve information across different modalities, we want to hear from you.
Our team values research excellence and encourages publishing in top-tier conferences while maintaining a strong focus on practical applications that benefit real users.
Key job responsibilities
- As an Applied Scientist, you will leverage your technical expertise and experience to demonstrate leadership in tackling large complex problems, setting the direction and collaborating with applied scientists and engineers to develop novel algorithms and modeling techniques to enable timely, relevant and delightful search experiences.
- Develop state-of-the-art multimodal search technology, including training novel retrieval and ranking models for images/videos, scaling models and optimizing performance, partnering with engineering to deploy and debug model performance in production, and building and scaling quality training data sets.
- Leverage Amazon's data and computing resources to accelerate advances in the state of the art in multimodal learning and information retrieval.
- Work backwards from customer needs and use that information to make trade-offs between different modeling approaches
- Collaborate with software engineering teams to integrate successful experimental results into complex Amazon production systems
- Report results to technical and business audiences in a manner that is statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment
- Drive best practices, helping to set high scientific and engineering standards on the team
- Promote the culture of experimentation and applied science at Amazon
