Senior LLMs Scientist for Fraud Detection & Scalable AI

DataVisor; Mountain View (CA); Presencial; USD 140.000 - 170.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

PTO

Stock Options

Health Benefits

Descripción de la vacante

DataVisor, a leading AI-powered fraud and risk platform, is seeking a skilled ML engineer to build and fine-tune large models that run on billions of events for real-time detection. You will design models on sequences, graphs, and multi-modal data; develop evaluation frameworks; and own model serving at production scale, with emphasis on performance and cost.

Collaborate with researchers and engineers to translate attacker insights into robust defenses, and communicate findings to customers and

Formación

• Master’s or PhD in CS, Statistics, Mathematics or a related quantitative field.
• 3+ years of hands-on experience in LLMs or fraud/adversarial risk.

Responsabilidades

• Build and fine-tune large models.
• Design and train models on sequences, graphs and multi-modal event data.
• Build evaluation frameworks for model quality, robustness and adversarial drift; own model serving at production latency and cost.
• Build AI agents and reusable tools that automate investigation, feature generation and reporting.
• Own full lifecycle of detection models: data exploration, prototyping, production, monitoring.

Conocimientos

LLMs and large models

Fraud and adversarial risk

Python programming

SQL

Spark / PySpark

PyTorch

Cloud platforms (AWS/GCP/Azure)

Data structures & algorithms

System design

Investigator mindset

Clear communication

Educación

Master’s or PhD in CS/Statistics/Math

Herramientas

Spark

PySpark

PyTorch

Cloud platforms

Descripción del empleo

DataVisor, a leading AI-powered fraud and risk platform, is seeking a skilled ML engineer to build and fine-tune large models that run on billions of events for real-time detection. You will design models on sequences, graphs, and multi-modal data; develop evaluation frameworks; and own model serving at production scale, with emphasis on performance and cost.

Collaborate with researchers and engineers to translate attacker insights into robust defenses, and communicate findings to customers and