At a Glance
- Tasks: Join us as an Applied Scientist to innovate AI-driven shopping experiences using machine learning and computer vision.
- Company: Be part of Amazon's dynamic team, shaping the future of conversational shopping technology.
- Benefits: Enjoy a collaborative environment with opportunities for hands-on projects and cutting-edge technology.
- Why this job: Make a real impact on customer experiences while working with top-tier professionals in AI and ML.
- Qualifications: PhD or Master's in CS, CE, ML; experience in deep learning and programming languages like Python.
- Other info: Work in London with a diverse team focused on multimodal user queries and innovative solutions.
The predicted salary is between 48000 - 84000 £ per year.
We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology powering Rufus, our AI-driven search and shopping assistant, helping customers with their shopping tasks at every step of their shopping journey.
This innovative role focuses on developing conversation-based, multimodal shopping experiences, utilizing multimodal large language models (MLLMs), generative AI, advanced machine learning (ML) technologies and computer vision.
Our mission in conversational shopping is to make it easy for customers to find and discover the best products to meet their needs by helping with their product research, providing comparisons and recommendations, answering product questions, enabling shopping directly from images or videos, providing visual inspiration, and more. We do this by pushing the SoTA in Natural Language Processing (NLP), Generative AI, Multimodal Large Language Model (MLLM), Natural Language Understanding (NLU), Machine Learning (ML), Retrieval-Augmented Generation (RAG), Computer Vision, Responsible AI, LLM Agents, Evaluation, and Model Adaptation., As an Applied Scientist on our team, you will be responsible for the research, design, and development of new AI technologies that will shape the future of shopping experiences. You will play a critical role in driving the development of multimodal conversational systems, in particular those based on large language models, information retrieval, recommender systems and knowledge graph, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers’ experiences. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems.
You will:
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Perform hands-on analysis and modelling of enormous multimodal datasets to develop insights into how to best help customers throughout their shopping journeys.
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Use deep learning, ML and MLLM techniques to create scalable language model centric solutions for building shopping assistant systems based on a rich set of structured and unstructured contextual signals.
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Innovate new methods for understanding, extracting, retrieving and summarising contextual information that allows for the effective grounding of MLLMs, considering memory, compute, latency and quality.
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Drive end-to-end MLLM projects that have a high degree of ambiguity, scale and complexity.
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Build models, perform offline and A/B test experiments, optimize and deploy your models into production, working closely with software engineers.
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Establish automated processes for large-scale data analysis and generation, machine-learning model development, model validation and serving.
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Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports and publish your work at internal and external conferences.
About the team
You will be part of a dynamic science team based in London, working alongside over 100 engineers, designers and product managers, focused on shaping the future of AI-driven shopping experiences at Amazon. This team works on every aspect of the shopping experience, from understanding multimodal user queries to planning and generating answers that combine text, image, audio and video.
PhD, or a Master’s degree and experience in CS, CE, ML or related field
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Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
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Experience programming in Java, C++, Python or related language
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Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
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Experience in building machine learning models for business application
PREFERRED QUALIFICATIONS
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Experience with generative deep learning models applicable to the creation of synthetic humans like CNNs, GANs, VAEs and NF
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Experience with popular deep learning frameworks such as MxNet and Tensor Flow
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Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms
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Have publications at top-tier peer-reviewed conferences or journals
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Experience leveraging and augmenting a large code base of computer vision libraries to deliver new solutions.
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Experience deploying solutions to AWS or other cloud platforms.
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Excellent communication skills, solid work ethic, and a strong desire to write production-quality code.
Applied Scientist, Rufus Experiences Science employer: Amazon.com, Inc
Contact Detail:
Amazon.com, Inc Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied Scientist, Rufus Experiences Science
✨Tip Number 1
Familiarize yourself with the latest advancements in multimodal large language models (MLLMs) and generative AI. Understanding these technologies will not only help you in interviews but also demonstrate your passion for the field.
✨Tip Number 2
Engage with the community by attending conferences or webinars focused on machine learning and AI. Networking with professionals in the industry can provide valuable insights and potentially lead to referrals.
✨Tip Number 3
Showcase your hands-on experience with deep learning frameworks like TensorFlow or MxNet through personal projects or contributions to open-source. This practical knowledge is crucial for the role and can set you apart from other candidates.
✨Tip Number 4
Prepare to discuss your experience with end-to-end machine learning projects, especially those involving ambiguity and complexity. Be ready to share specific examples of how you've tackled challenges in previous roles.
We think you need these skills to ace Applied Scientist, Rufus Experiences Science
Some tips for your application 🫡
Highlight Your Machine Learning Expertise: Make sure to emphasize your strong background in machine learning, particularly in deep learning and multimodal large language models. Provide specific examples of projects or research that showcase your skills in these areas.
Showcase Relevant Experience: Detail your experience with programming languages such as Java, C++, or Python. Mention any relevant projects where you built machine learning models for business applications, especially those involving computer vision or generative AI.
Communicate Clearly: Since the role involves communicating results to both technical and non-technical audiences, ensure your application materials are clear and concise. Use straightforward language to explain complex concepts and your contributions to past projects.
Include Publications and Contributions: If you have publications in top-tier peer-reviewed conferences or journals, be sure to include them in your application. This demonstrates your commitment to advancing the field and your ability to contribute to the scientific community.
How to prepare for a job interview at Amazon.com, Inc
✨Showcase Your Machine Learning Expertise
Be prepared to discuss your experience with machine learning, particularly in deep learning models and architectures. Highlight specific projects where you've applied these techniques, especially in relation to multimodal datasets or language models.
✨Demonstrate Problem-Solving Skills
Expect to face complex, ambiguous problems during the interview. Practice articulating your thought process for tackling such challenges, including how you would approach model optimization and data analysis in a real-world scenario.
✨Communicate Clearly and Effectively
Since you'll be working with both technical and non-technical teams, practice explaining your work in simple terms. Prepare to present your past projects and findings in a way that is accessible to all audiences.
✨Familiarize Yourself with Relevant Technologies
Brush up on the latest advancements in generative AI, computer vision, and large language models. Be ready to discuss how these technologies can be applied to enhance shopping experiences and how you've used them in your previous work.