Staff Machine Learning Scientist
New
G
GoDaddyApplied AI and Machine Learning
This position may be a hybrid or fully remote position, as decided by your manager. 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.Full-TimeStaff
SalaryBay 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
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Job Details
- Required Skills
- Machine LearningA/B testingNLPLLMGenerative AI
Requirements
- 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.
Responsibilities
- 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.
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