Software Development Manager

• 7 years of experience working directly within engineering teams
• Experience partnering with product OR program management teams
• 3 years of people management experience, managing engineers
• 3 years of experience architecting and designing (architecture, design patterns, reliability and scaling) of new and current systems

BASIC QUALIFICATIONS· Bachelor's degree in Computer Science, Computer Engineering or equivalent combination of technical education and experience

• 7 years of experience with at least 3 years in a Software Development Manager role.
• 2 years experience building distributed systems
• A strong track record of project delivery for large, cross-functional projects

Interested in Machine Learning? Amazon SageMaker is a fully managed Machine Learning platform that makes it easy to build ML models, manage them, and integrate them with custom applications for online predictions. SageMaker https://aws.amazon.com/sagemaker/) takes away the heavy-lifting normally associated with large-scale Machine Learning implementations, so that developers and scientists can focus on the truly creative work of modeling and solving the business problem at hand.

As an engineering leader, you will own the innovation in the space of ML Platforms, building compelling functionality for the Amazon SageMaker Service. You will be responsible for leading a team of strong engineers in design, development, test, and deployment of distributed systems and big data solutions. A successful candidate will have an established background in developing distributed systems, a strong technical ability, excellent project management skills, great communication skills, and a motivation to achieve results in a fast paced environment.

Key Responsibilities:

• Responsible for the over-all systems development life cycle
• Management and execution against project plans and delivery commitments; Manage the day-to-day activities of the engineering team within an Agile/Scrum environment
• Management of departmental resources, staffing, mentoring, and enhancing and maintaining a best-of-class engineering team
• Work closely with the engineers to architect and develop the best technical design and approach
• Report on status of development, quality, operations, and system performance to management
• Customer engagement and product road map definition

If you have an entrepreneurial spirit, know how to deliver, are deeply technical, highly innovative and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you.

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.

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. 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.

SageMaker is one of the fastest growing AWS services. While ML is no longer a new concept, applying ML in production to solve real-world problems at scale is still in this infancy. Today, most of the AI/ML projects don’t see the light of day because of the challenges in productionizing the model. Established ML teams are facing various challenges in automating their entire ML workflows. Our mission is to build a collection of platform services and tools in SageMaker to help customer in making their ML projects successful. We solve challenges in ML like what DevOps solves for software engineering over the past decade. You will lead the founding team in building an end-to-end ML pipeline platform in SageMaker. Your work will enable customers to build and run entire ML pipelines on Amazon SageMaker in a fully-managed, highly scalable and highly available manner.