ML Engineer - Ads Performance & GenAI Infra

DataJobs

Seattle (WA)

On-site

USD 144,000 - 194,000

Full time

9 days ago
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Benefits offered by this job

Sign-on payments
RSUs
Health insurance
401(k) matching
Paid time off
Parental leave

Job summary

Amazon’s Advertising & Marketing Performance Intelligence (AMPI) team seeks an ML engineer to design, deploy, and scale production-grade ML and GenAI solutions for automated decision-making and personalized marketing communications. This onsite Seattle role combines ML engineering with infrastructure work on large training systems.

You’ll optimize AWS AI/ML infrastructure, build reusable assets, and collaborate with data scientists to improve training workflows, monitoring, and reliability on

Qualifications

  • 3+ years of software development experience (non-internship).
  • Bachelor’s degree in computer science or equivalent.
  • 1+ year building ML infrastructure for online systems.

Responsibilities

  • Process data with scientists to scale ML and GenAI workloads while optimizing costs.
  • Build reusable assets for ML, optimization, and GenAI adoption.
  • Design production-grade distributed training systems for ML workloads.
  • Optimize AWS AI/ML infrastructure for training efficiency and GPU use.
  • Create monitoring and debugging tooling for training reliability.
  • Collaborate to evaluate designs and prototype new GenAI models.

Skills

Software development
System design
ML basics

Education

Bachelor's degree in Computer Science

Tools

AWS Bedrock
SageMaker
Hugging Face
LangChain
Amazon Q

Job description

Amazon’s Advertising & Marketing Performance Intelligence (AMPI) team seeks an ML engineer to design, deploy, and scale production-grade ML and GenAI solutions for automated decision-making and personalized marketing communications. This onsite Seattle role combines ML engineering with infrastructure work on large training systems.

You’ll optimize AWS AI/ML infrastructure, build reusable assets, and collaborate with data scientists to improve training workflows, monitoring, and reliability on

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