Software Development Engineer

GoDaddy · United States, Remote

Hybrid Full-time Not specified Engineering

Job Description

Location Details:

At GoDaddy the future of work looks different for each team. Some teams work in the office full-time; others have a hybrid arrangement (they work remotely some days and in the office some days) and some work entirely remotely.

This is a remote position, so you’ll be working remotely from your home. You may occasionally visit a GoDaddy office to meet with your team for events or meetings.

This position is not eligible to be performed in Alaska, Mississippi, North Dakota, or the Virgin Islands.

GoDaddy is not currently considering candidates for this role in California, Seattle, or NYC.

Join Our Team

At GoDaddy, we’re building the future of AI at scale. The GenAI team powers the platform, tools, and services that enable generative AI innovation across the company, from intelligent routing and AI spend optimization to the next generation of AI-powered products and experiences.

As part of our team, you’ll help empower engineers, product teams, and business partners to securely build, deploy, and scale AI solutions that drive real customer and business impact. Our platform supports 100+ large language models across leading providers including OpenAI, Anthropic, Google, and AWS, delivering 99.99% uptime and enabling thousands of employees across dozens of teams to innovate with confidence.

If you’re excited about solving complex technical challenges, shaping the future of AI infrastructure, and building platforms that amplify the impact of an entire organization, we’d love to hear from you.

Compensation:

The estimated pay ranges for this role are listed below. In addition to base pay, this role may be eligible for other forms of compensation, which may include a corporate bonus and/or equity awards, subject to the terms of applicable plans and individual eligibility.

Bay Area (Santa Clara, San Francisco) and Los Angeles:$111,000-$167,000 USDAustin, D.C. Metro, CA (non-Bay Area), HI, IL, MA, NH, OR, VA, WA:$96,000-$144,000 USDNew York City Metro, Kirkland/Seattle:$102,000-$153,000 USDAll other US locations not previously listed:$85,500-$128,500 USD

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Benefits

Design, develop, and maintain high-quality software solutions using JavaScript and Python, supported by comprehensive unit and integration testing
Collaborate across the full software development lifecycle, including planning, architecture, implementation, deployment, and ongoing maintenance of both client-side and server-side applications
Drive engineering excellence through thoughtful code reviews, mentoring, and continuous improvements to code quality, reliability, and development best practices
Own outcomes end-to-end, delivering scalable, production-ready solutions that prioritize reliability, performance, and operational excellence while minimizing on-call burden
Contribute to Open-Source projects (See more GoDaddy open source at godaddy.github.io .)
Containerization & Orchestration: Familiarity with Docker and AWS ECS for building, deploying, and managing containerized applications
Security & Privacy Expertise: Experience implementing secure data handling practices and working with privacy and compliance frameworks
Developer Platform Experience: Experience building internal developer tools, platforms, or infrastructure that improve engineering productivity and efficiency

Requirements

2+ years of professional software engineering experience developing and delivering scalable, high-quality applications
2+ years of professional experience in Python, JavaScript, React, and Node.js, with hands-on experience in observability, monitoring, and maintaining reliable production systems
Strong computer science fundamentals, including software design principles, data structures, algorithms, and coding best practices
Experience with DevOps practices and deployment processes, including CI/CD pipelines and tools such as GitHub Actions, Docker, AWS, Webpack, and Artifactory
Experience with AI/ML Infrastructure, working with large language models (LLMs), retrieval-augmented generation (RAG) systems, or broader AI/ML infrastructure and tooling