AWS Lambda is a serverless compute service that runs customer code in response to events and automatically manages the underlying compute resources. These events may include changes in state or an update, such as a user placing an item in a shopping cart on an ecommerce website. AWS Lambda automatically runs code in response to multiple events, such as HTTP requests via Amazon API Gateway, modifications to objects in Amazon Simple Storage Service (Amazon S3) buckets, table updates in Amazon DynamoDB, and state transitions in AWS Step Functions.
Lambda runs the code on high availability compute infrastructure and performs all the administration of compute resources. This includes server and operating system maintenance, capacity provisioning and automatic scaling, code and security patch deployment, and code monitoring and logging.
Our scientists enjoy solving real-world problems that, quite frankly, have not been solved at scale anywhere before. Along the way, you’ll get opportunities to be a fearless disruptor, prolific innovator, and a reputed problem solver—someone who truly enables computer science, statistical modeling, operations research and machine learning to create significant impacts.
As a Scientist, you will bring statistical modeling and machine learning advancements to data analytics for customer-facing solutions in complex industrial settings. You will be working in a fast-paced, cross-disciplinary team of researchers who are leaders in the field. You will take on challenging problems, distill real requirements, and then deliver solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products.
Notable research areas include:
- Cloud and systems: https://www.amazon.science/research-areas/cloud-and-systems
- Machine Learning: https://www.amazon.science/research-areas/machine-learning
- Operations research and optimization: https://www.amazon.science/research-areas/operations-research-and-optimization
Inclusive Team Culture
Here 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.
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.
- PhD (or MS + 3 years of industry research experience) in Electrical Engineering, Computer Science, Computer Engineering, Mathematics, Statistics, Physics, Bioinformatics, Operations Research or a related quantitative discipline.
- Familiar with statistics, statistical modeling, machine learning and/or serverless computing.
- Proficient in at least one programming / scripting language such as Python, R, Java, C/C++, Scala.
- Hands-on experience deploying machine learning models following software engineering best practices.
- Experience building and productionizing machine learning models with PyTorch and/or other Neural Network and Deep Learning packages and tools.
- Published and/or presented papers at top conferences associated with relevant research topics including but not limited to ICASSP, ICML, KDD, CVPR, EMNLP, NeurIPS, etc.
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.