Interested in this AI/ML Engineer role at GoDaddy?
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About This Role
- R023694
- United States
- Engineering
- Regular
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 position may be a hybrid or fully remote position, as decided by your manager. If designated as hybrid, you’ll divide your time between working remotely from your home and an office location, so you should live within commuting distance. If designated as remote, you’ll be working remotely from your home and may occasionally visit a GoDaddy office to meet with your team for events or meetings. Your hiring manager can share more about this role’s hybrid or remote designation.
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...
Join GoDaddy's Applied AI and Machine Learning team, where you'll help shape the future of how millions of small business owners leverage AI to grow and run their businesses. Our team develops machine learning models and generative AI solutions that directly influence revenue, customer experience, personalization, pricing, and business outcomes at scale. As a Staff Machine Learning Scientist, you'll operate with a high degree of autonomy, partner closely with engineers and product leaders, and drive innovative AI initiatives from concept to production in a fast\-moving environment that values purposeful action and measurable impact.
What you'll get to do...
- Lead the development and deployment of machine learning and generative AI solutions that solve complex customer and business problems at scale.
- Drive innovation across key strategic initiatives including personalization, pricing optimization, experimentation, agentic AI, and simulation\-based modeling.
- Partner closely with Engineering, Product Management, and business stakeholders to translate ambiguous problems into impactful AI\-powered solutions.
- Influence technical direction, modeling approaches, and AI strategy while mentoring other scientists and engineers across the organization.
- Measure, analyze, and optimize the business impact of machine learning solutions through experimentation and data\-driven decision making.
Your experience should include...
- Advanced knowledge of machine learning theory and demonstrated success applying ML techniques to real\-world business challenges.
- Hands\-on experience building, deploying, and scaling production AI, machine learning, or generative AI solutions.
- Strong experience leveraging modern AI tools and large language model ecosystems, including platforms such as Claude, GPT, Bedrock, or equivalent technologies.
- Proven ability to communicate complex technical concepts to both technical and non\-technical audiences and influence cross\-functional stakeholders.
- Track record of operating independently, driving initiatives through ambiguity, and delivering measurable business outcomes.
You might also have...
- Experience developing personalization, recommendation, ranking, or customer targeting systems.
- Background building conversational AI, chatbots, NLP applications, or retrieval\-augmented generation (RAG) solutions.
- Experience with agentic AI architectures, autonomous workflows, and emerging generative AI technologies.
- Expertise designing and evaluating experimentation frameworks, A/B testing methodologies, or simulation\-based models.
- Experience working on customer\-facing AI products within SaaS, e\-commerce, technology, or high\-scale consumer environments.
*We encourage you to apply even if your experience or skillset doesn’t align perfectly with every requirement. We value a wide range of backgrounds and transferable skills, and we are excited to support learning and growth.*
About us... GoDaddy is empowering everyday entrepreneurs around the world by providing the help and tools to succeed online, making opportunity more inclusive for all. GoDaddy is the place people come to name their idea, build a professional website, attract customers, sell their products and services, and manage their work. Our mission is to give our customers the tools, insights, and people to transform their ideas and personal initiative into success. To learn more about the company, visit About Us.
At GoDaddy, we know diverse teams build better products—period. Our people and culture reflect and celebrate that sense of diversity and inclusion in ideas, experiences and perspectives. But we also know that’s not enough to build true equity and belonging in our communities. That’s why we prioritize integrating diversity, equity, inclusion and belonging principles into the core of how we work every day—focusing not only on our employee experience, but also our customer experience and operations. It’s the best way to serve our mission of empowering entrepreneurs everywhere, and making opportunity more inclusive for all. To read more about these commitments, as well as our representation and pay equity data, check out our Diversity and Pay Parity annual report which can be found on our Diversity Careers page.
We also embrace our diverse culture and offer a range of Employee Resource Groups (Culture). Have a side hustle? No problem. We love entrepreneurs! Most importantly, come as you are and make your own way.
*GoDaddy is proud to be an equal opportunity employer. GoDaddy will consider for employment qualified applicants with criminal histories in a manner consistent with local and federal requirements.* *Refer to our full* *EEO policy.*
Our recruiting team is available to assist you in completing your application. If they could be helpful, please reach out to [email protected].
Colorado Residents: In any materials you submit, you may redact or remove age\-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.
GoDaddy doesn’t accept unsolicited resumes from recruiters or employment agencies.
Compensation \& Benefits:
What We Offer:
Working at GoDaddy offers many benefits, including competitive pay, generous time off, parental and wellness leave, healthcare, retirement savings program, and much more. Offerings vary by location.
This role is eligible for a comprehensive benefits package, which includes medical, dental, and vision insurance, a 401(k)\-retirement plan, paid sick time, paid flexible time off, paid parental leave, life insurance, short\- and long\-term disability, AD\&D insurance, mental health or EAP programs, remote or hybrid work options, paid holidays, paid Wellness days, tuition assistance, adoption, surrogacy, and fertility benefits, dependent daycare and backup care benefits, Employee stock purchase plan, financial education and advice; and other benefits in accordance with GoDaddy’s benefit plans and applicable law.
Actual compensation and benefits eligibility will be determined based on permissible, non\-discriminatory factors such as skills, experience, and geographic location.
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:
$182,000—$273,000 USD
Austin, D.C. Metro, CA (non\-Bay Area), HI, IL, MA, NH, OR, VA, WA:
$157,000—$235,000 USD
New York City Metro, Kirkland/Seattle:
$166,800—$250,200 USD
All other US locations not previously listed:
$140,000—$210,000 USD
Salary Context
This $140K-$273K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At GoDaddy, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($206K) sits 6% below the category median. Disclosed range: $140K to $273K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
GoDaddy AI Hiring
GoDaddy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $273K - $273K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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