Senior Applied Scientist, AWS Vulnerability Management

DESCRIPTION

We are open to hiring candidates to work out of one of the following locations:

Arlington, VA, USA | Austin, TX, USA | New York, NY, USA | Seattle, WA, USA

At AWS Security, we're on a mission to revolutionize how we manage security vulnerabilities at scale for host operating systems and third party software. We're seeking a Senior Applied Scientist specializing in generative AI to join our team and lead the charge in developing provable security solutions that protect our customers and empower our builders.

In this role, you'll be at the forefront of addressing one of the most critical challenges in cloud security today: managing security risks due to vulnerable code in host operating systems or third party software. With the increasing frequency and sophistication vulnerabilities, your work will be crucial in safeguarding AWS environments against vulnerabilities that can arise from external components integrated into our infrastructure.

You'll leverage your expertise in generative AI and machine learning to develop innovative approaches for identifying, assessing, and remediating security risks in third-party software. Your solutions will help bridge the gap between rapid innovation and robust security practices, ensuring that the software packages our builders use are not only efficient but also comply with AWS security standards.

As a key member of our team, you'll collaborate closely with cross-functional teams to integrate your scientifically-validated security measures into existing workflows. Your work will directly impact AWS's ability to respond quickly and efficiently to vulnerabilities, addressing challenges such as package naming ambiguity between internal and external sources.

We're looking for someone who can think strategically about both mid-term tactical improvements and long-term security innovations. You'll be instrumental in establishing a data-driven decision framework that will guide our security measures, ensuring they're not only effective but also optimized for builder efficiency.

Your role extends beyond just technical innovation. As a senior member of the team, you'll be a thought leader in the field of AI-driven security, contributing to the broader Amazon Science community and potentially publishing your findings in top-tier scientific venues. You'll also mentor team members, sharing your expertise and helping to cultivate the next generation of security innovators at AWS.

This position offers a unique opportunity to make a substantial impact across AWS, contributing to the overall resilience and trustworthiness of our security programs. Your work will be central to maintaining customer trust and delivering secure, delightful customer experiences across all of Amazon's products and services.

If you're passionate about solving complex challenges, have a deep understanding of generative AI and machine learning, and are excited about the prospect of shaping the future of cloud security, we want to hear from you. Join us in our mission to set new standards for security through AI innovation at Amazon.

Key job responsibilities
1. Lead research and development of generative AI solutions to address security challenges in Vulnerability Management.
2. Develop and implement data-driven decision frameworks to analyze patterns in the package environment and quantify the impact of security improvements on builder productivity.
3. Collaborate closely with cross-functional teams to integrate scientifically-validated security measures into existing workflows.
4. Design and conduct experiments to solve complex security problems
5. Develop scalable, AI-driven solutions to efficiently respond to vulnerabilities in software packages.
6. Create and implement long-term strategic plans for integrating generative AI into AWS security practices.
7. Publish research findings in scientific venues and contribute to the broader Amazon Science community.
8. Mentor team members and provide expertise on a range of machine learning topics related to security.

A day in the life
You will be joining the AWS Vulnerability Management (AVM) team, focusing on vulnerability remediation at the host operating systems and third party software. We must scale through utilization of Amazon AI tooling and machine learning modeling. Your day to day is solve security problems that causes builders not to patch (or update software) in time, and how we leverage data we have (or need) to build security solutions to resolve these problems.

About the team
Diverse Experiences
Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why Amazon Security?
At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores
Inclusive Team Culture
In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices.
Training & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

BASIC QUALIFICATIONS

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning