Sr. Applied Scientist - AWS Catalog, AWS Catalog

Job summary

You will design and implement formal semantics and permissions models for AWS data catalogs to ensure secure and precise data access. You will also collaborate with cross-functional teams to drive scientific strategy and deliver complex, scalable components.

Job details

This is a chance to work at the front of data security and governance with Amazon scale impact on customers. As a Sr. Applied Scientist III, you bring your education, experience, and expertise in formal methods, programming languages, and automated reasoning to the permissions model behind AWS's data catalog, so that customers can state precisely who may see which data and trust that the answer holds wherever that data is read. Experience with LLMs, multi-agent workflows, and structured guardrails that raise determinism in LLM output is a plus. In this role you join a strong group of applied scientists in a highly supportive environment with real cognitive safety and room to grow. Key job responsibilities You combine deep expertise in programming languages and automated reasoning to design the language and the formal semantics customers use to express permissions over catalog resources, down to the level of individual columns and rows. The model has to stay correct, compact, expressive enough for real customer intent, explainable when it denies a request, and exhaustively testable. Much of the difficulty is navigating ambiguity to balance those properties against each other. You write clear narratives and documentation that enumerate the design choices and build consensus quickly. You lead design and delivery of scientifically complex components that are rarely revisited once shipped and that deliver measurable customer benefit. The problems are concrete: proving that a change to a policy cannot expand access beyond what the author intended, deciding whether two policies written in different models grant the same thing, showing that a fast evaluation path on the request hot path agrees with the reference semantics, and keeping permissions faithful to intent as schemas and data evolve underneath them. You maintain detailed knowledge of your team's systems and proactively drive improvements in efficiency and consistency across team boundaries. You influence your team's science and business strategy, collaborating with Applied Scientists, Engineers, and Product Managers across identity, storage, analytics, and machine learning teams, and contributing to roadmaps, goals, and priorities. You harmonize discordant views and build consensus through thoughtful feedback. Beyond your team, you apply advanced techniques to bottleneck problems and are regarded as a reliable, creative problem solver. You further AWS's academic influence through publications, talks, and advancing the state of the art in data security, governance, programming languages, and automated reasoning. About the team The team owns the permissions layer of AWS's data catalog. That covers 1/ the model and formal semantics customers use to author permissions across catalogs, databases, tables, columns, and rows, 2/ the evaluation path that turns those permissions into an authorization decision on every request, under latency budgets that leave no room for a slow answer, 3/ the detection and resolution of divergence between what a customer intended and what is actually enforced as resources change over time, and 4/ converging existing permission models onto one, including the reasoning needed to show that a migration preserves a customer's access boundaries. The team works closely with identity, storage, and analytics teams across AWS. Basic Qualifications: - 6+ 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 Preferred Qualifications: - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. - Experience with large scale distributed systems such as Hadoop, Spark etc. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations [https://amazon.jobs/content/en/how-we-hire/accommodations] for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. As a total compensation company, Amazon's package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well-being. We thank all applicants for their interest, however only those interviewed will be advised as to hiring status. CAN, BC, Vancouver - 195,900.00 - 327,200.00 CAD annually

What you’ll do

You will design and implement formal semantics and permissions models for AWS data catalogs to ensure secure and precise data access. You will also collaborate with cross-functional teams to drive scientific strategy and deliver complex, scalable components.

Requirements

Candidates must have a PhD or a Master's degree with at least 6 years of applied research experience in machine learning. Proficiency in programming languages such as Java, C++, or Python and experience with deep learning methods are required.

Benefits

• Health insurance
• Medical insurance
• Dental insurance
• Vision insurance
• Prescription insurance
• Basic life insurance
• AD&D insurance
• Registered retirement savings plan
• Deferred profit sharing plan
• Paid time off
• Restricted stock units

Listed skills

• Machine learning · Preferred
• Java · Preferred
• C++ · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Formal methods
• Programming languages
• Automated reasoning
• Data security
• Data governance
• Java
• C++
• Python
• Machine learning
• Deep learning
• Distributed systems
• Neural networks
• Policy modeling
• System architecture
• Scientific research
• Influencing Skills
• Deep Learning Methods
• Automated Reasoning
• Workflow Management
• Time Off Management
• Apache MXNet
• Programming Languages
• Multi-Agent Systems
• Amazon Web Services
• Applied Research
• Business Software
• C++ (Programming Language)
• Creative Problem Solving
• Data Security
• Deterministic Methods
• R (Programming Language)
• Governance
• Apache Hadoop
• Problem Solving
• Python (Programming Language)
• Language Experience Approach
• Machine Learning
• Medical Prescription
• NumPy (Python Package)
• TensorFlow
• Restricted Stock
• SciPy
• Business Strategies
• Testability
• Authorization (Computing)
• Scikit-Learn (Python Package)
• Formal Methods
• Honesty

Job areas

• Technology
• Science & Research
• Data & Analytics
• Software
• Engineering
• Catalog Manager
• Research Scientist
• Physical and Engineering Science Technicians Not Elsewhere Classified
• Physical Scientists, All Other

More jobs from Amazon