ML/AI Scientist RecSys and Ad-Tech, AWS AI

Key Skills

Java Programming, Design of Experiment, Python Programming, C++, Framework, SQL, AWS, Deep Learning, PyTorch, TensorFlow, MxNet, C# Programming

Job Description

Job summary
Who are we and what we do ?
Amazon Personalize, an applied research team situated within the larger AWS AI organization, is looking to hire applied machine learning (ML) scientist to work on a variety of important applied ML problems in the area of personalization & recommendations, audience modeling and ad targeting. Our applied ML/AI group is entrusted with developing state-of-the-art generalized deep learning based models that can scale to many millions of items, hundreds of millions of users, react to extremely fast changing catalogue, preserve local-global taste characteristics, optimize for long term rewards, learn the causal effect of our recommendation on user experience and do within session adaptation -- all within a few tens of milli-seconds of latency. Other related problems we are working on include audience modeling, user targeting (item promotions, user churn prediction) and home page optimization.

Who is an ideal candidate ?
You are an autonomous contributor in the science team. You solve complex problems, applying appropriate ML technologies and best practices. You work with your team on ambiguous problem areas in existing or new ML initiatives. You understand the business impact of your solutions and you show extreme good judgement when making technical trade-offs between short term technology/operational needs and long term business needs. You help balance customer requirements with team requirements. You take on new projects and constantly try to improve the existing models and infrastructure necessary for offline and online experimentation and iteration.

As an applied research scientist in AWS AI working on recommender system and user modeling problems, you are expected to be on top of the literature and various open source challenges and datasets. You are expected to maintain an understanding of industry and technology trends in the area of deep learning, natural language processing and recommendation & personalization. You will be expected to contribute to the larger science community by either giving presentations at workshops, summits, conferences and/or publishing in top-tier conferences.

Our Organization at Large:
We take pride in being at the forefronts of deep learning research and its applications to many product offerings. As a member of this org, you will interact closely with our customers and with the academic community. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists.
Our sister teams work on variety of other interesting problems spanning computer vision, time series forecasting, machine translation, speech recognition, language understanding and deep learning.

Inclusive Team CultureHere at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.

Work/Life Balance
Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.

Mentorship & Career Growth
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.

BASIC QUALIFICATIONS

• PhD/MS degree in electrical and computer engineering, computer science, operations research, statistics, or a related quantitative field.
• 1+ years of hands on experience in working on applied ML problems in an industrial setting.
• Programming experience with at least one modern language such as Python, Java, C++, or C# including object-oriented design.
• Proficiency in model development, model validation and model implementation for large-scale applications.
• 1+ years of experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems.

PREFERRED QUALIFICATIONS

• Ph.D. degree in Electrical & Computer Engineering, Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field.
• Experience working with Deep Learning frameworks (PyTorch TensorFlow, MXNet.)
• Strong Computer Science fundamentals in data structures, problem solving, algorithm design and complexity analysis;
• Ability to convey mathematical results to non-science audience;
• Strength in clarifying and formalizing complex problems;
• Experience with defining research and development practices in an applied environment;
• Industry experience in the area of recommender systems, natural language processing, forecasting or other related areas;
• Strong publication record at top conferences and journals.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.