Applied Scientist II, DevOps Guru, AWS AI

Key Skills

Java Programming, Analytical and Problem solving, Design, Python Programming, C++, Framework, SQL, DevOps, Machine learning techniques, Deep Learning, PyTorch, TensorFlow, Algorithms, MxNet, C# Programming, Common data structures

Job Description

AWS AI Labs is looking to hire applied machine learning (ML) scientist to work on a variety of important applied ML problems in the area of anomaly detection, timeseries modeling/forecasting, causal inference and natural language processing Our applied ML/AI group is entrusted with developing state-of-the-art generalized statistical learning algorithms for understanding multivariate timeseries data, detect anomalies, do root cause analysis of the anomalies (causal analysis, attribution, Granger causality) and provide recommendations (recourse) as well as predict the anomalies that may occur in future (proactive anomaly detection as against reactive).

Who is an ideal candidate ?

You are an autonomous contributor in the science team. You solve complex problems, applying appropriate ML technologies / statistical analysis 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 Labs working on DevOps Guru system, 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 statistical anomaly detection, timeseries forecasting, deep learning, natural language processing and causal inference. 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 devOps systems, anomaly detection, forecasting or other related areas;
• Strong publication record at top conferences and journals.