Conduct applied research in Generative AI, exploring emerging foundation models, architectures, and inference strategies to address enterprise AI use cases.
Lead investigation and experimentation across areas such as: Agent‑based and multi‑step reasoning workflows, Retrieval‑augmented generation (RAG), Long‑context modeling and information synthesis, Prompting strategies and controllability of generative outputs.
Design and execute rigorous experimental frameworks to evaluate model quality, robustness, latency, cost, and reliability using: Automated metrics, Human‑in‑the‑loop feedback, LLM‑as‑a‑judge and comparative evaluation methodologies.
Create benchmarks and build reproducible pipelines to compare vendor models, open‑source alternatives, and internal approaches.
Rapidly prototype research ideas using modern GenAI tooling such as: Vertex AI / Google Agent Development Kit, LangChain / LangGraph, RAG frameworks, Hugging Face ecosystems, OpenAI APIs.
Translate research findings into deployable GenAI capabilities, working closely with engineering and MLOps teams to ensure scalability, observability, and responsible AI practices.
Contribute to the enterprise GenAI roadmap by identifying gaps, risks, and opportunities informed by ongoing research and industry trends.
Partner with stakeholders across Data Science, Technology, and Business teams to align research directions with real‑world operational challenges.

Requirements

Masters and 5+ years of industry experience OR PhD and 3+ years of industry experience in machine learning or data science with 1+ years focused on GenAI.
Strong proficiency in Python and modern AI/ML frameworks
Demonstrated experience designing experiments, running evaluations, and drawing conclusions from empirical results.
Hands-on experience with Generative AI frameworks and platforms, including Vertex AI, ADK, LangChain/LangGraph, RAG pipelines, Hugging Face, and OpenAI APIs.
Deep understanding of: Agent workflows and tool‑augmented LLMs, Prompt engineering and model controllability, Retrieval‑augmented generation (RAG) design trade‑offs, Generative model evaluation and benchmarking techniques
Experience collaborating with engineering and MLOps teams to transition prototypes into production systems.
Strong written and verbal communication skills, including the ability to explain research findings, limitations, and trade‑offs to both technical and non‑technical audiences.
Authorization to work in the United States without company sponsorship.

Benefits

Health insurance
401(k)
Short-term or annual bonuses
Long-term incentives
On-the-spot recognition

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonGenerative AImachine learningdata scienceexperiment designmodel evaluationbenchmarking techniquesprompt engineeringmodel controllabilityRAG design trade-offs

Soft Skills

strong written communicationstrong verbal communicationcollaborationstakeholder engagementexplanation of research findings

Certifications

MastersPhD