Senior AI Scientist
New
Based in the United StatesFull-TimeSenior
SalaryCompetitive salary and equity package
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonArtificial IntelligenceMachine LearningRESTful APIsMLOps
Requirements
- 5+ years of experience building and shipping production AI/ML systems used by real external users.
- Strong backend engineering skills, including experience building APIs, pipelines, logging, monitoring, and production infrastructure.
- Proven experience deploying and evaluating LLM-based or agentic AI systems in real-world environments.
- Advanced proficiency in Python and experience with modern ML/AI frameworks.
- Track record of owning projects end to end in ambiguous, fast-paced environments with high autonomy.
- Strong systems thinking and ability to balance research exploration with production reliability.
- Excellent communication skills, with the ability to explain complex AI concepts to non-technical stakeholders and leadership.
- Experience with cloud environments (e.g., AWS) and modern MLOps practices.
- Preferred: experience with fine-tuning, knowledge distillation, quantization, or agentic workflows.
- Preferred: background in consumer health, wellness, or personalization AI products.
Responsibilities
- Lead the design, development, and deployment of production-grade AI and ML systems powering consumer-facing AI health experiences.
- Build and maintain backend services, APIs, and infrastructure supporting LLM and agentic AI pipelines at scale.
- Design evaluation frameworks to measure reliability, grounding, safety, and performance of LLM-based systems in production.
- Develop and deploy agentic AI systems, iterating on performance through experimentation, monitoring, and optimization.
- Own end-to-end project delivery from problem definition and research to deployment, observability, and continuous improvement.
- Collaborate with Product and Engineering teams to translate user needs and data insights into shipped features.
- Improve model performance through techniques such as fine-tuning, distillation, quantization, and prompt/system optimization.
- Identify new opportunities to apply AI to enhance personalization, health insights, and user engagement.
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