ML Engineering Intern

Duration: ~ 6 months (~July 2026 – December 2026)

Full Time: M-F, 40 hr/ week

Salary Range: $50,000 - $62,000 (Depending on prior ML experience, degree(s) obtained)

Team: ML Products

Geo Comply’s detection systems catch location spoofing, identity fraud, suspicious device patterns, and more. We collect a LOT of data, and the ML Products team’s purpose is to transform it into production pipelines that run reliably, at scale, across clients.

We’re hiring an ML Engineering Intern to join our ML Products team. You’ll take validated detection logic and turn it into production batch and streaming pipelines on Databricks, writing the data processing, feature engineering, tests, and monitoring.

• Convert validated detection code into well‑tested production pipelines (Python, PySpark, Databricks).
• Build and ship batch and streaming pipelines that compute risk intelligence signals.
• Design testing and validation frameworks that catch problems before they reach production.
• Propose, design, and extend internal AI‑assisted skills that automate recurring engineering and operational work for the team (deployment automation, signal investigation, channel monitoring, ticket coordination).
• Support multi‑client rollout of detection pipelines: schema changes, config, deployment orchestration.
• Debug production issues with the team and improve monitoring and observability.
• Write clean code, participate in code reviews, and document your work so it outlasts your internship.

• Degree in Computer Science, Software Engineering, Data Science, or equivalent experience.
• Strong software engineering foundation: data structures, algorithms, Git, clean code habits.
• Familiarity with at least one cloud data platform (Databricks, GCP, or AWS) and with databases (relational or No
  
  SQL).
• Comfortable working in modern AI‑assisted development workflows (Claude Code or equivalent) or eager to learn.
• Clear communicator: written and verbal across technical and non‑technical teams.

• Prior internship or co‑op experience involving production code, data pipelines, or ML systems.
• Exposure to PySpark or distributed data processing.
• Experience with pipeline orchestration (Databricks Workflows, Airflow) or streaming systems.
• Background in fraud detection, risk intelligence, or anti‑abuse work.
• Experience building internal developer tools, CLI tools, bots, or automation scripts.

• Prior ML work experience – personal projects and interest are sufficient.
• PySpark or distributed data processing – Python skills are enough to start.
• Fraud detection domain knowledge – you’ll learn on the job.
• Production deployment experience – many interns have none at start.
• A background solely in ML – the role blends engineering and ML.

• Direct production impact from week one: code runs against real fraud signals serving live clients.
• ML and engineering interface – work at the boundary between data science research and production engineering.
• AI‑assisted workflows are baseline; you’ll use and extend them.
• Strong mentorship: day‑to‑day technical guidance and weekly 1:1s with the team manager.
• Conversion‑track framing: high‑performing interns historically lead to full‑time SWE/MLE roles.

$50,000 – ~$62,000 (annualized, prorated to the ~6‑month placement). The range reflects experience level, educational background, and seniority.

We welcome applicants of all backgrounds, communities, experiences, beliefs, and identities. We encourage qualified applicants to apply regardless of how strictly they meet the hard requirements.