Senior GenAI Research Engineer (AI Agent), Digital Health

Lab Summary: 

We are an interdisciplinary team with an aim to empower providers, consumers, and clinical researchers. We develop GenAI/LLM-based health applications for engaging customer experience, create prognostic biomarkers by applying clinically explainable AI on advanced sensing technology, explore use cases by co-innovating with our partners, and design services through pilots that eventually turn into groundbreaking commercial, consumer-grade products that are used every day. Our work has been internationally recognized with 100+ peer-reviewed publications.

Position Summary:

Samsung Research America Digital Health Team is looking for an outstanding Senior-level ML Research Engineer with solid Generative AI/Large Language Model technology background and extensive industry experience in building, scaling and optimizing ML pipelines. You must have a strong track-record of success in commercializing GenAI/LLM products. You will play a key role in delivering innovation in digital health domain, create technologies, deploy and validate novel technologies, and transfer code to production. You will also author scientific publications in top-tier computing venues. This team is the right fit for you if you love working with the latest technologies in LLMs, MLOps and ML more broadly.

You will be a core part of a passionate team charged with developing, incubating, and launching a portfolio of digital health product concepts that will disrupt the healthcare paradigm. By leveraging smart phones, wearables, embedded devices and the IoT in the health/wellness domain, your work will significantly benefit real-world patients, seniors, physicians and care givers. Samsung’s unique advantage in the consumer electronics market and growing focus on digital health will provide you with exciting technical challenges and a rewarding career experience.

Position Responsibilities:

• Lead the development of our next-generation autonomous agent ecosystem. In this role, you will bridge the gap between state-of-the-art ML research and production-grade consumer applications, specifically focusing on multi-agent orchestration and standardized communication protocols. You will be responsible for building resilient, interoperable agents that solve complex, multi-step tasks at scale while adhering to emerging industry standards like A2A and MCP.
• This role requires a unique blend of deep theoretical knowledge in LLMs and a proven track record of shipping production-grade AI features to millions of users.
• Agentic System Architecture: Design and deploy sophisticated multi-agent systems capable of long-horizon reasoning, planning, and autonomous task completion.
• Protocol Standard Adoption: Implement and optimize agentic workflows using the Model Context Protocol (MCP) for seamless tool and data integration.
• Interoperability Leadership: Architect cross-platform collaboration using the Agent-to-Agent (A2A) protocol to enable secure communication and task delegation between independent agents.
• End-to-End Product Delivery: Translate research breakthroughs into high-impact consumer features, managing the full lifecycle from prototyping to large-scale production deployment.
• Advanced Evaluation & Safety: Develop robust evaluation frameworks—including LLM-as-a-Judge and scenario-based testing—to ensure agent reliability, safety, and alignment.
• Collaborate cross-functionally with the product and engineering teams to define priorities and influence the product roadmap.

Required Skills:

• MS or PhD in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, or equivalent combination of education, training, and experience
• 5+ years of experience in ML, with a strong track record of shipping consumer-facing AI products at scale
• Experience building and deploying scalable GenAI/LLM applications, with tools such as VertexAI, HuggingFace, Langchain and OpenAI
• GenAI Expertise: Deep understanding of transformer architectures, LLM fine-tuning, and modern generative AI methodologies
• Agent Expertise: Proven track record in building and deploying autonomous agents, including experience with frameworks like LangGraph, CrewAI, or Semantic Kernel
• Standard Mastery: Hands-on experience with MCP for connecting agents to external business tools/data and A2A for multi-agent coordination
• Technical Stack: Expert proficiency in Python and deep learning frameworks (e.g., PyTorch)
• Evaluation Expertise: Demonstrated experience building scalable, data-driven frameworks to measure LLM performance, safety, and robustness in real-world scenarios
• Strong interpersonal and collaboration skills, ability to present complex information in an understandable and compelling manner,