ML Data Platform Engineer II

Amazon

Palo Alto (CA)

On-site

USD 165,000 - 224,000

Full time

9 hours ago
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Benefits offered by this job

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

Job summary

Amazon.com Services LLC in Palo Alto is seeking a senior software engineer to advance data-driven ML platforms and scalable services. You will work at the intersection of machine learning, big data, and distributed systems, enabling teams across Amazon to improve search quality and inference capabilities.

You will use AWS AI services and core backend systems to observe and optimize applications from pre-deployment through production at scale.

Qualifications

  • 3+ years of non-internship professional software development experience.
  • 2+ years of non-internship design or architecture of new and existing systems experience.
  • Experience programming with at least one software programming language.
  • Bachelor's degree or equivalent in Computer Science.

Responsibilities

  • Lead development of services and infrastructure at the intersection of machine learning, big data, and distributed systems.
  • Provide a world-class platform for Amazon Search engineers to observe and introspect their applications pre and post deployment.
  • Build scalable data-intensive infrastructure that processes large volumes of logs, catalogs, transactions, and telemetry signals.
  • Collaborate with partners to advance Amazon product search across regions and categories.

Skills

Software development experience
System design / architecture
Programming language experience

Education

Bachelor's degree in Computer Science

Job description

Amazon.com Services LLC in Palo Alto is seeking a senior software engineer to advance data-driven ML platforms and scalable services. You will work at the intersection of machine learning, big data, and distributed systems, enabling teams across Amazon to improve search quality and inference capabilities.

You will use AWS AI services and core backend systems to observe and optimize applications from pre-deployment through production at scale.

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