Staff AI Scientist, Personalization & LLM Systems (Remote)

oneapp; United States; Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

Stock options

Health benefits

401(k) match

Remote-friendly (US)

Job summary

oneapp is seeking a staff-level applied scientist to own personalization systems across our all-in-one financial platform. The role blends research‑grade modeling with production deployment to shape how millions of users interact with banking, credit, savings, and other financial services.

You will lead end‑to‑end AI initiatives, collaborating with product, engineering, and analytics to build scalable personalization, search, and retrieval systems that enhance discovery and user satisfaction

Qualifications

• Seven+ years of experience building and productionizing ML/AI models with measurable impact.
• Hands-on experience with LLM-powered apps, agentic systems, and classical ML (ranking, recommendations, personalization).
• Collaborates with product, engineering, and analytics to deploy AI solutions.

Responsibilities

• Design, build, and deploy ML/AI models and LLM systems for customer-facing experiences.
• Develop personalization and recommendation systems across product surfaces.
• Design and optimize search/retrieval to improve discoverability and relevance.
• Lead AI initiatives from ideation to deployment, sharing best practices across the team.
• Productionize LLM-powered applications alongside traditional ranking, recommendations, and personalization.

Skills

LLM‑powered applications

Agentic systems

Ranking & personalization

Cross‑functional collaboration

Education

Degree in CS/Data science/math or related

Job description

oneapp is seeking a staff-level applied scientist to own personalization systems across our all-in-one financial platform. The role blends research‑grade modeling with production deployment to shape how millions of users interact with banking, credit, savings, and other financial services.

You will lead end‑to‑end AI initiatives, collaborating with product, engineering, and analytics to build scalable personalization, search, and retrieval systems that enhance discovery and user satisfaction