Senior ML Scientist: Real-Time Ad Response & Optimization

Amazon; Palo Alto (CA); On-site; USD 192,000 - 260,000; Full time

Benefits offered by this job

Health insurance

RSUs (restricted stock units)

Paid time off

Parental leave

Job summary

Amazon.com Services LLC in Palo Alto, CA seeks a Machine Learning Applied Scientist to advance generative AI-powered advertising solutions for Sponsored Products and Brands. You will build scalable models, run A/B tests, and ship end-to-end ML systems that impact customer experiences at scale.

Candidate will collaborate with software engineers and researchers, specialize in deep learning, and contribute to productionized ML pipelines across platforms, with a strong emphasis on responsible AI and

Qualifications

• 3+ years of building ML models for business applications.
• PhD or Master\'s with 6+ years of applied research experience.
• Proficient in Java, C++, Python or related language.
• Experience with neural deep learning methods.

Responsibilities

• Conduct deep data analysis to derive insights and identify opportunities.
• Develop scalable ML models and optimization strategies.
• Run regular A/B experiments and perform statistical analysis.
• Collaborate with software engineers to deploy end-to-end solutions.
• Improve large-scale data analytics, training, deployment and serving.
• Research new ML modeling to optimize business areas.

Skills

3+ years building ML models

Deep learning

Java

C++

Python

Education

PhD or Master\'s in a relevant field

6+ years applied research experience

Tools

TensorFlow

PyTorch

Spark MLLib

Hadoop

Job description

Amazon.com Services LLC in Palo Alto, CA seeks a Machine Learning Applied Scientist to advance generative AI-powered advertising solutions for Sponsored Products and Brands. You will build scalable models, run A/B tests, and ship end-to-end ML systems that impact customer experiences at scale.

Candidate will collaborate with software engineers and researchers, specialize in deep learning, and contribute to productionized ML pipelines across platforms, with a strong emphasis on responsible AI and