Software Development Engineer, ML Infrastructure Team

Benefits

Parental leave

6 weeks From the posting source

Non-birth-parent leave

6 weeks From the posting source

Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

Not verified

Childcare support

Not verified

Learning budget

Not verified

Verification

Source-linked checked Jun 7, 2026

Salary

$165K-$224K From the posting source

401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

74% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source

Seniority

Mid From the posting source

Schedule

Shift type

Not verified

Weekend work

Not verified

Company

Equity

Offered Verified - SEC 10-K source

Application

Cover letter

Not verified

Assessment

Not verified

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Software Development Engineer, ML Infrastructure Team Seattle, Washington, USA Want to help drive the success of Machine Learning technologies at AWS? We seek a Software Development Engineer II for the ML Infrastructure team to build the platforms that guarantee top performance of AWS ML and HPC technologies. Our performance data directly influences launch decisions for new EC2 instance types and has visibility at senior leadership. Join us as we expand the AWS offerings for AI, including Trainium, Neuron and the Elastic Fabric Adapter (EFA). You'll build CI/CD systems, orchestrate GPU clusters, create performance dashboards, and develop AI-powered automation - all to ensure latest ML networking software ships with confidence. Key job responsibilities Build and maintain infrastructure that monitors and reports on functionality and performance of massive testing workloads run at scale across multiple GPU instance types. Use Jenkins, internal Amazon CI/CD tools, Linux, and public AWS products to automate testing and delivery of ML networking libraries - including collective communication frameworks, network transport layers, and GPU communication libraries. Write Python code that orchestrates large clusters, runs benchmarks and ML applications across a matrix of instance types, operating systems, and software stack versions. Use AWS Managed Grafana and Athena to digest performance data and build dashboards that catch functional and performance regressions before they reach customers. Build automation using LLMs to analyze test failures and surface actionable insights to developers. Contribute to cross-team readiness for new instance type launches by delivering performance data that shapes go/no-go decisions. Manage the complexity of

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