Staff ML Scientist
New
This position may be a hybrid or fully remote position, as decided by your manager.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
- Data AnalysisMachine 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.
- Experience developing personalization, recommendation, ranking, or customer targeting systems (preferred).
- Background building conversational AI, chatbots, NLP applications, or retrieval-augmented generation (RAG) solutions (preferred).
- Experience with agentic AI architectures, autonomous workflows, and emerging generative AI technologies (preferred).
- Expertise designing and evaluating experimentation frameworks, A/B testing methodologies, or simulation-based models (preferred).
- Experience working on customer-facing AI products within SaaS, e-commerce, technology, or high-scale consumer environments (preferred).
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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