IFS

Data Scientist - Agentic AI Systems - IFS Loops

Palo Alto, California, United States · On-site · Full-time

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About the role

Description from the employer’s posting. Check the employer’s page for the current version.

EmployerIFS
LocationPalo Alto, California, United States
Work styleOn-site
Employment typeFull-time
Employer posted2 months ago

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

Show full description

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
Independent – You operate autonomously and drive projects end-to-end
Collaborative – You thrive in cross-functional teams and open discussion

What We’re Offering

Salary Range: $140,000 to $150,000 annually + bonus
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

M/F/Disabled/Vet VEVRAA Federal Contractor. We are a Drug-Free Workplace. Interested candidates should apply at: www.ifs.com/about/careers-at-ifs

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. VEVRAA Federal Contractor, Equal Opportunity Employer

We embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles, while also valuing inclusive workplace experiences. By fostering a sense of community, we drive innovation, strengthen connections, and nurture belonging. Our commitment ensures you can work in a way that suits you best, while also engaging with colleagues to share ideas and build meaningful relationships.

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