Applied Scientist, AWS Causality Lab - AutoGluon

Key Skills

Java Programming, Python Programming, AJAX, SQL, AWS, TensorFlow, MxNet, NeurIPS

Job Description

Job summary

AWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building open-source automated ML solutions.

We are seeking an experienced Applied Scientist for the AutoGluon team. This is a role that combines science knowledge (around machine learning, natural language processing, computer vision), technical strength, and product focus. You will be at the heart of a growing and exciting focus area for AWS!

Key job responsibilities

• Develop novel ML systems and algorithms while working with the engineering team to integrate them into our open-source projects
• Interact closely with the open-source, academic and research communities
• Work with other acclaimed engineers and world famous scientists
• Publish in scientific conferences and journals, create white papers, write blogs, and have high visibility in the industry

A day in the life

*Inclusive Team Culture*

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. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

*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. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.

About the team

Our team’s mission is to democratize machine learning by creating powerful open-source tools like AutoGluon for solving practical ML problems and revolutionize the rate and ease ML practitioner’s progress from problem formulation to deployed solution. Our vision is to advance the state-of-the-art in automated ML to become the go-to tool for solving the vast majority of ML problems. The team specializes in developing popular open-source software libraries like AutoGluon, GluonCV, GluonNLP, and Deep Graph Library (DGL). Building these solutions requires a solid foundation in machine learning infrastructure and deep learning technologies.

BASIC QUALIFICATIONS

• Progress towards a completed PhD in Computer Science, Mathematics, Physics, Statistics or any other field with strong quantitative focus
• Demonstrated experience in transforming theoretical concepts into consumer products
• Proven coding experience both prototyping new methods and building industry-standard software
• Record of publications at well-regarded conferences and in associated journals in either machine learning (ML), causality, statistics, information theory or probability theory
• Experience with deep learning frameworks such as PyTorch, TensorFlow, JAX, MXNet

PREFERRED QUALIFICATIONS

• Published papers at ICML, NeurIPS, ICLR, AISTATS, UAI, AAAI, IJCAI, KDD, CVPR, ICCV, ECCV, ACL, EMNLP, MLSys, OSDI, Eurosys, SC, or similar top-tier conferences and journals
• Research experience in areas including: AutoML, model ensembling, meta-learning, time series analysis, model explainability, transfer learning, deep learning, graph neural networks, HPC, or large-scale distributed systems
• Practical experience in areas including: ML competitions, open-source software development, and ML benchmarking