Staff Research Scientist, AI Agents & LLMs
SnowflakeUS-WA-BellevuePosted 2 April 2026
Job Description
Staff Research Scientist, AI Agents & LLMs
At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.
THE MISSION
We are seeking a Staff Research Scientist to serve as a technical pillar within our AI research organization. You will not just execute on roadmaps—you will define them.
At the intersection of autonomous agents and large language models (LLMs), you will build the models and systems that turn enterprise data into self-directed, decision-making agents—shifting from passive data access to autonomous execution.
Our team operates at the frontier of enterprise AI, delivering both state-of-the-art models and production systems, including:
- Arctic LLM: Enterprise foundation models optimized for performance and efficiency
- Arctic-Text2SQL: Post-trained reasoning models with frontier-level quality at a fraction of cost and latency
- Snowflake Intelligence: Brought research to production through agentic innovations in multi-step reasoning, Deep Research for structured and unstructured insights, and system optimization
- Arctic Inference: High-performance LLM serving stack (Shift Parallelism, SwiftKV), open-sourced and powering Snowflake Cortex
- Arctic Long-Context Training: Enables 1M+ token context training on a single H200 GPU
- Agent World Models (AWM): RL training across 1,000+ synthetic environments for tool-using agents
We focus on core challenges such as reliable reasoning, long-horizon planning, grounded generation, and scalable model and agent development.
IN THIS ROLE, YOU WILL:
- Define research direction in Agentic AI and LLMs
- Develop models: train, fine-tune, and align models for enterprise reasoning and tool use
- Advance autonomous agents: drive state-of-the-art capabilities in multi-step reasoning and tool use
- Advance the stack: retrieval, grounding, memory, and multi-agent coordination
- Establish evaluation standards for reliability, safety, and efficiency
- Drive research breakthroughs into production within Snowflake’s platform
- Mentor and lead to amplify the team’s technical impact
- Contribute to the field through publications and open source
WE’RE LOOKING FOR:
- Ph.D. in Computer Science or a related field with a strong publication record
- Expertise in LLM development (training, fine-tuning, alignment, or post-training)
- Experience with agentic systems (multi-agent systems, tool use, or agent optimization)
- Strong systems thinking under real-world constraints (latency, cost, reliability)
- Proven technical leadership and end-to-end ownership
- Ability to translate research into impactful systems
WHY SNOWFLAKE?
Frontier AI, Full Stack
Work across the full stack—from foundation models (Arctic LLM, Arctic Text2SQL) to agent systems (Snowflake Intelligence) and inference/training infrastructure (Arctic Inference, Arctic Training).
State-of-the-Art, Real Impact
Drive cutting-edge research that ships—powered by massive enterprise data and production workloads.
Hard, Meaningful Problems
Build autonomous systems for reliable reasoning over enterprise data—where correctness, cost, and latency all matter.
Open Research Culture
Publish, open-source, and help shape the field.
Snowflake is growing fast, and we are scaling our team to accelerate AI innovation.
Are you ready to build the brain for the Enterprise Data Cloud?
Every Snowfla ... (truncated, view full listing at source)
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