Machine Learning Engineer Intern contributing to AI solutions for financial services. Engaging in hands - on ML projects and real production issues in a hybrid working environment.
About the role
- AI/ML Engineering Intern developing large-scale AI systems at DataVisor for real-time fraud detection. Collaborating with experienced engineers and data scientists to build AI applications and data pipelines.
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
Requirements
- 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)
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
- Comp Range, $25 - $70/hour
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