AI/Machine Learning Engineering Intern (MS/Ph.D. New Grad)
About DataVisor
DataVisor is the world’s leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.
Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!
Role Summary
We are seeking highly motivated, newly graduated or soon-to-graduate MS or Ph.D. students in Computer Science, Machine Learning, Data Science, or related fields to join us as AI / ML Engineering Interns.
This internship is ideal for candidates who are eager to learn how large-scale AI systems are built and deployed in production. You will work closely with experienced engineers and data scientists to help build the Intelligence Layer and Data Consortium that power DataVisor’s real-time fraud detection platform.
This internship focuses on distributed systems, data pipelines, machine learning infrastructure, and applied AI, including exposure to agentic flows and large language models (LLMs).
What You’ll Do
• Data Engineering & Pipelines
• 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
• Distributed Systems & Scalability
• 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
• AI Applications & Agentic Flows
• 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
• Machine Learning Pipelines
• Help deploy and monitor pipelines for unsupervised and supervised ML models
• Assist with integrating models into real-time scoring APIs and decision engines
• Privacy & Security
• Learn best practices for privacy-first system design, including tokenization and hashing to protect sensitive data
• Cross-Functional Collaboration
• Work alongside Data Science, Product, and Engineering teams to test ideas, validate models, and ship production features
• 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)
Preferred (Nice-to-Have)
• 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)
• 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
• Comp Range, $25 - $70/hour
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DataVisor is the world’s leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.
Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!
Role Summary
We are seeking highly motivated, newly graduated or soon-to-graduate MS or Ph.D. students in Computer Science, Machine Learning, Data Science, or related fields to join us as AI / ML Engineering Interns.
This internship is ideal for candidates who are eager to learn how large-scale AI systems are built and deployed in production. You will work closely with experienced engineers and data scientists to help build the Intelligence Layer and Data Consortium that power DataVisor’s real-time fraud detection platform.
This internship focuses on distributed systems, data pipelines, machine learning infrastructure, and applied AI, including exposure to agentic flows and large language models (LLMs).
What You’ll Do
• Data Engineering & Pipelines
• 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
• Distributed Systems & Scalability
• 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
• AI Applications & Agentic Flows
• 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
• Machine Learning Pipelines
• Help deploy and monitor pipelines for unsupervised and supervised ML models
• Assist with integrating models into real-time scoring APIs and decision engines
• Privacy & Security
• Learn best practices for privacy-first system design, including tokenization and hashing to protect sensitive data
• Cross-Functional Collaboration
• Work alongside Data Science, Product, and Engineering teams to test ideas, validate models, and ship production features
• 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)
Preferred (Nice-to-Have)
• 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)
• 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
• Comp Range, $25 - $70/hour
Apply tot his job
Apply To this Job