Principal Machine Learning Engineer, Agentic AI
New
Z
ZillowReal Estate AI
U.S. employees may live in any of the 50 United States, with limited exceptions.Full-TimePrincipal
SalaryUSD 194200 - 326600 / year
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Job Details
- Experience
- 7+ years
- Required Skills
- Machine LearningNLPLLMLangChain
Requirements
- Master's degree or above, or equivalent experience in Computer Science, Electrical Engineering, or a related field
- Emphasis on foundational LLM, agentic AI, reinforcement learning, AI planning, or natural language processing
- 7+ years of hands-on work building large-scale, high-impact solutions
- Recent 2 years building agent-based systems, multi-agent collaboration, or similar paradigms
- Experience developing dialogue systems capable of long conversations, multi-step reasoning, context-rich decision-making
- Experience deploying and scaling AI services capable of handling hundreds of millions of daily interactions
- High-level expertise in maintaining high availability, low latency, and robust fault tolerance
- Ability to marry state-of-the-art technology with large-scale engineering
- Strong communication skills for distilling research into actionable insights for executives
Responsibilities
- Leverage frameworks like AgentSDK, and LangChain/LangGraph to design, prototype, and develop multi-agent systems that are capable of highly autonomous and context-aware interactions
- Leverage advanced GenAI models including reasoning models, real-time voice API, etc, to build agentic prototypes and later on convert them into product-level agentic skills and deploy to users
- Mentor and guide engineers in using the right technologies for agentic AI solutions and foster a culture of innovation and responsible AI usage
- Distill complex research findings and system designs into actionable insights for diverse audiences—including executives
- Remain on the cutting edge of agentic AI emerging paradigms, driving product innovation
- Serve as the focal point for applied science projects, driving alignment on timelines, and prioritization
- Continuously refine processes for experimentation, A/B testing, and production rollouts to balance rapid innovation with reliability and responsible deployment
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