Data Scientist - Agentic AI Systems - IFS Loops at IFS
Palo Alto, CA
$140,000–$150,000from the description
Jul 15, 2026
Palo Alto, CA
Jul 21, 2026
What this job asks for AI summary
A research-oriented data science role focused on building and evaluating AI agent systems — covering areas such as planning, memory, retrieval, and reasoning. The work involves designing experiments, prototyping models, analyzing interaction data, and collaborating with engineering and product teams to turn findings into deployable features. Best suited to someone with a graduate-level ML background and hands-on experience with large language models and agentic frameworks.
Mid level · 4+ years · National · Master's required · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (8)
No work location or metro is specified in the posting; IFS is a global company and the role may be remote or hybrid in the US. The posting references hybrid work flexibility but does not confirm fully remote.
The degree requirement is Masters or PhD — Masters is set as the minimum hard gate since the JD lists both with 'or'.
scikit-learn and PyTorch are listed together as examples of required libraries ('scikit-learn, PyTorch, LangChain or any similar agentic framework'); they are distinct tools and emitted separately, each with the other as an alternative since the 'or similar' qualifier applies to the whole list.
LangChain is listed in the required qualifications with 'or any similar agentic framework'; alternatives are populated with the named agent frameworks from the Preferred section (AutoGen, LangGraph, OpenAgents).
Reinforcement Learning and Fine-Tuning appear in the required qualifications under 'Familiarity with LLMs, RAG, Reinforcement Learning, Fine-Tuning' — 'familiarity with' is softer language but the items sit in the Minimum Qualifications block, so they are treated as hard gates.
LLM post-training methods (SFT, DPO, RLHF, GRPO) are listed under Preferred Qualifications and are marked accordingly.
Agent frameworks (AutoGen, OpenAgents, LangGraph) appear under Preferred Qualifications and are marked accordingly.
The salary range of $140,000–$150,000 annually plus bonus is stated explicitly; bonus is not quantified.
Read the full posting
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