Data Scientist - Agentic AI Systems - Loops
About The Position
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.
Requirements
- 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
Nice To Haves
- 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
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
Benefits
- Flexible paid time off, including sick and holiday
- Medical, dental, & vision insurance
- 401K with Company contribution
- Flexible spending accounts
- Life insurance and disability benefits
- Tuition assistance
- Community involvement and volunteering events
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What This Job Offers
Job Type
Full-time
Career Level
Senior
