Data Scientist - Agentic AI Systems - IFS Loops
Designs and experiments with AI agents that learn, plan, and act in dynamic environments using LLMs, RAG, and reinforcement learning, while analyzing user-agent interaction data.
- Compensation
- $140K–$150K / yr
- Location
- Palo Alto, California, us
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Contract
- Experience
- 4+ years
- Category
- Data science
- Posted
Skills
Must have
- Masters or PhD in Computer Science, Applied Math, Statistics, or related quantitative field
- 4+ years of experience in data science, applied ML, or AI research
- Strong Python and SQL skills; experience with libraries like scikit-learn, PyTorch, LangChain or any similar agentic framework
- Familiarity with LLMs, retrieval-augmented generation (RAG), Reinforcement Learning, Fine-Tuning
- Comfort with ambiguity, fast iteration cycles, and self-directed research
- Excellent communication and storytelling skills — you can explain complex models to others clearly
- Curious – You explore the edges of what’s possible with AI agents
- Action-biased – You ship prototypes quickly and iterate based on evidence
- Scientific – You bring structure to messy problems and back claims with data
- Independent – You operate autonomously and drive projects end-to-end
- Collaborative – You thrive in cross-functional teams and open discussion
Nice to have
- Experience working with agent frameworks (AutoGen, OpenAgents, LangGraph, etc.)
- Background in decision-making models, memory systems, or multi-agent coordination
- Exposure to vector databases, embeddings, and custom RAG pipelines
- Experience building evaluation frameworks or simulators for agent performance
- Experience with LLM Post Training – SFT, DPO, RLHF, GRPO
Full description
We are seeking a Data Scientist with a strong research mindset to help shape the future of agentic AI systems. This role blends deep analytical thinking with fast-paced experimentation and model development, where your insights will directly inform how AI agents reason, plan, and act in dynamic environments.
You’ll work on a cross-functional team exploring how AI agents learn from real-world data, adapt to user intent, and interact autonomously—or collaboratively—with users and systems. If you’re a self-starter who thrives on ambiguity, builds fast, and can bridge theory with practical outcomes, this role is for you.
Key Responsibilities
- Design and run applied research initiatives that inform the behavior and learning loops of AI agents
- Build and evaluate models for planning, memory, retrieval, reasoning, or tool use in agentic systems
- Develop internal tools and pipelines to test agent behavior across different environments
- Analyze structured and unstructured data from user-agent interactions, logs, and experiments
- Rapidly prototype and test hypotheses to improve agent performance and reliability
- Collaborate with engineering, product, and design to translate insights into deployable features
- Communicate findings clearly and concisely to both technical and non-technical audiences
Minimum Qualifications
- Masters or PhD in Computer Science, Applied Math, Statistics, or related quantitative field
- 4+ years of experience in data science, applied ML, or AI research
- Strong Python and SQL skills; experience with libraries like scikit-learn, PyTorch, LangChain or any similar agentic framework
- Familiarity with LLMs, retrieval-augmented generation (RAG), Reinforcement Learning, Fine-Tuning
- Comfort with ambiguity, fast iteration cycles, and self-directed research
- Excellent communication and storytelling skills — you can explain complex models to others clearly
PreferredQualifications
- Experience working with agent frameworks (AutoGen, OpenAgents, LangGraph, etc.)
- Background in decision-making models, memory systems, or multi-agent coordination
- Exposure to vector databases, embeddings, and custom RAG pipelines
- Experience building evaluation frameworks or simulators for agent performance
- Experience with LLM Post Training – SFT, DPO, RLHF, GRPO
Who You Are
- Curious – You explore the edges of what’s possible with AI agents
- Action-biased – You ship prototypes quickly and iterate based on evidence
- Scientific – You bring structure to messy problems and back claims with data