Applied Scientist II: DynamoDB Capacity & Data Placement

Amazon; Austin (TX); On-site; USD 142,800 - 193,200; Full time

Benefits offered by this job

RSUs

Sign-on payments

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon DynamoDB is seeking an Applied Scientist to advance capacity utilization and data placement across the fleet. You will build models and algorithms to inform capacity profile decisions and placement policy, partnering with DynamoDB teams to bring inputs into production decisions.

This customer-obsessed, self-driven role tackles ill-defined problems, requiring you to frame them, extend state-of-the-art methods, and drive measurable impact with a bias for action.

Qualifications

• PhD or Master’s with 4+ years in CS/CE/ML.
• Experience in patents or publications at top-tier peer-reviewed conferences or journals.
• Experience programming in Java, C++, Python.
• Experience in algorithms, data structures, numerical optimization, data mining, parallel and distributed computing.

Responsibilities

• Identify capacity usage optimization opportunities across the DynamoDB fleet.
• Quantify the customer and cost impact of each opportunity.
• Model the scaling bottlenecks of the service and characterize how they constrain placement and utilization.
• Develop data placement approaches that balance customer experience against capacity utilization.
• Partner with the DynamoDB performance team to validate impact with production data.
• Build components that integrate directly into production systems or support large systems making placement decisions.
• Author or co-author papers for internal or external peer‑reviewed venues when the work is novel.

Job description

Amazon DynamoDB is seeking an Applied Scientist to advance capacity utilization and data placement across the fleet. You will build models and algorithms to inform capacity profile decisions and placement policy, partnering with DynamoDB teams to bring inputs into production decisions.

This customer-obsessed, self-driven role tackles ill-defined problems, requiring you to frame them, extend state-of-the-art methods, and drive measurable impact with a bias for action.