Do you love decomposing problems to develop machine learning (ML) products that impact millions of people around the world? Would you enjoy identifying, defining, and building ML software solutions that revolutionize how businesses operate? The Global Practice Organization in Professional Services at Amazon Web Services (AWS) is looking for a Software Development Engineer II to build, deliver, and maintain complex ML products that delight our customers and raise our performance bar. You’ll design fault-tolerant systems that run at massive scale as we continue to innovate best-in-class services and applications in the AWS Cloud. Key job responsibilities Our ML Engineers collaborate across diverse teams, projects, and environments to have a firsthand impact on our global customer base. You’ll bring a passion for the intersection of software development with generative AI and machine learning. You’ll also: - Solve complex technical problems, often ones not solved before, at every layer of the stack. - Design, implement, test, deploy and maintain innovative ML solutions to transform service performance, durability, cost, and security. - Build high-quality, highly available, always-on products. - Research implementations that deliver the best possible experiences for customers. A day in the life As you design and code solutions to help our team drive efficiencies in ML architecture, you’ll create metrics, implement automation and other improvements, and resolve the root cause of software defects. You’ll also: - Build high-impact ML solutions to deliver to our large customer base. - Participate in design discussions, code review, and communicate with internal and external stakeholders. - Work cross-functionally to help drive business solutions with your technical input. - Work in a startup-like development environment, where you’re always working on the most important stuff. About the team The Global Practice Organization for Analytics is a team inside the AWS Professional Services Organization. Our mission in the Global Practice Organization is to be at the forefront of defining machine learning domain strategy, and ensuring the scale of Professional Services' delivery. We define strategic initiatives, provide domain expertise, and oversee the development of high-quality, repeatable offerings that accelerate customer outcomes. Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 85,000 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 16 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 harmony. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here. We are a customer-obsessed organization—leaders start with the customer and work backwards. They work vigorously to earn and keep customer trust. As such, this is a customer facing role in a hybrid delivery model. Project engagements include remote delivery methods and onsite engagement that will include travel to customer locations as needed. 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. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future. This is a customer-facing role and you will be required to travel to client locations and deliver professional services as needed. We are open to hiring candidates to work out of one of the following locations: Atlanta, GA, USA | Austin, TX, USA | Boston, MA, USA | Chicago, IL, USA | Herndon, VA, USA | Minneapolis, MN, USA | New York, NC, USA | San Diego, CA, USA | San Francisco, CA, USA | Seattle, WA, USA
September 23, 2022Methods for learning from noisy data, using phonetic embeddings to improve entity resolution, and quantization-aware training are a few of the highlights.
