AI/Machine Learning Engineering Intern (MS/Ph.D. New Grad)

DataVisor is the world's leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. The AI/Machine Learning Engineering Intern will assist experienced engineers and data scientists in building the Intelligence Layer and Data Consortium for real-time fraud detection, focusing on distributed systems, data pipelines, and machine learning infrastructure.

Responsibilities

• Assist in building and maintaining high-throughput data pipelines using technologies such as Spark, Kafka, or Flink
• Help process and aggregate real-time signals (e.g., device fingerprints, behavioral data) into shared intelligence systems
• Learn to design and optimize backend systems that support large-scale, real-time decisioning
• Contribute to improving system performance, reliability, and latency under high transaction volumes
• Support the development of AI applications and agentic workflows using state-of-the-art LLMs (e.g., OpenAI, Anthropic, Google)
• Experiment with natural language interfaces, intelligent rule suggestions, and automated strategy generation
• Help deploy and monitor pipelines for unsupervised and supervised ML models
• Assist with integrating models into real-time scoring APIs and decision engines
• Learn best practices for privacy-first system design, including tokenization and hashing to protect sensitive data
• Work alongside Data Science, Product, and Engineering teams to test ideas, validate models, and ship production features

Skills

• Recently graduated or currently completing an MS or Ph.D. in Computer Science, Machine Learning, AI, Data Science, or a related field
• Passionate about learning how real-world AI systems are built at scale
• Comfortable working with complex technical problems and eager to grow through mentorship
• Strong programming skills in Python
• Familiarity with at least one of the following: distributed systems, machine learning, data engineering, or backend development
• Academic or project experience with big data frameworks (Spark, Kafka, Flink) is a plus
• Understanding of core ML concepts (supervised / unsupervised learning)
• Coursework or project experience with: LLMs, RAG architectures, LangChain, or vector databases
• Cloud platforms (AWS) and containers (Docker)
• Stream processing or real-time systems
• Interest in fraud, risk, or security domains (not required)

Benefits

• Hands-on experience working on production-scale AI systems
• Mentorship from senior engineers and data scientists
• Exposure to cutting-edge agentic AI and LLM applications
• Opportunity for full-time conversion based on performance and business needs

Company Overview

Company H1B Sponsorship