Senior Machine Learning Scientist, Agentic AI & GenAI Systems (Growth Marketing)
$173,000–$242,500 year
On-siteSeattle, Washington, United States
Job Summary
Architect, build, and ship enterprise-scale GenAI, RAG, and multi-agent systems end-to-end, including frontend, backend, and user interfaces. Design hierarchical multi-agent ecosystems with Interactive Generative UIs, dashboards, and safety/observability features. Develop memory architectures for short-term contextual and long-term episodic data, and lead hands-on implementation of RAG pipelines, vector memory systems, and agent orchestration frameworks. Train, fine-tune, adapt, and distill LLMs including RLHF/DPO for production-ready chatbots and GenAI products. Build multimodal pipelines integrating vision, audio, text, and structured data, while constructing large-scale behavioral embedding systems to power personalization and next-best-action recommendations. Mentor engineers, set technical standards, and represent the organization externally through open-source contributions and publications. Collaborate with product and engineering teams to create intuitive, user-friendly interfaces ensuring seamless end-to-end experiences.
Required Qualifications
- 10+ years in software engineering, ML, and AI systems, with production GenAI deployments
- Deep expertise in LLM training, adaptation, distillation, RLHF/DPO, and RAG systems
- Solid foundation in NLP and experience with multimodal AI systems (vision-language models)
- Proven experience building and operating multi-agent AI platforms with observability and safety frameworks (self-hosted orchestration using frameworks such as LangGraph integrated with LLM APIs such as Claude)
- Strong background in distributed GPU training and inference, cloud infrastructure (AWS/Azure), container orchestration, and ML tooling
- Demonstrated ability to lead end-to-end AI product development and collaborate with product and design teams to ship user-facing features
- Excellent communication skills, able to present complex architecture and product concepts to executives
Desired Qualifications
- PhD in Computer Science, Machine Learning, or a related field
- Recognized industry presence through publications, patents, talks, or open-source contributions in LLMs, RAG, or agentic systems
- Experience integrating multimodal LLM systems (vision, audio, music, structured data)
- Leadership in GenAI safety, evaluation, testing, and monitoring
- Strong cross-disciplinary fluency in modeling, infrastructure, product, and design
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