What You'll Actually Do: (Key Responsibilities)

• 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, and ticket coordination. This is high-leverage internal work that compounds across the rest of the team.

• Support multi-client rollout of detection pipelines: schema changes, config, deployment orchestration.

• Debug production issues with the team. Improve monitoring and observability along the way.

• Write clean code, participate in code reviews, and document your work so it outlasts your internship.

• Embrace Stretch Opportunities (The Fun Part!): As part of GeoComply's Early Talent Program, expect to be pushed. You’ll be given special, high-value projects outside the immediate scope of your role, a chance to explore new skills, test your limits, and adapt to new challenges across the business.

Who we're looking for: (Hard Requirements)

Nice to haves:

• Prior internship or co-op experience involving production code, data pipelines, or ML systems.

• Exposure to PySpark or distributed data processing is a plus.

• 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.

What you don't need: (We'll teach you)

• Prior ML Work Experience: Personal projects and a genuine interest is almost preferable for this team; you’ll have no bad habits to train you out of.

• PySpark or distributed data processing work experience. If you know Python well, we'll get you up to speed on PySpark and databricks.

• Fraud detection domain knowledge. You'll build that context fast because you'll be working on real signals from day one.

• Production deployment experience. Plenty of strong interns have never shipped code to production before starting here.

• A background in solely ML.

Don't Apply If:

• You prefer to WFH. Our Early Talent Program focuses heavily on professional development and network building that requires in person connection and regular office attendance.

• You want to work on a single thing for six months. This team moves fast, priorities shift weekly. The interns who thrive here are the ones who drive their own clarity when the roadmap shifts under them.

• You only want to do "pure ML." The title says ML Engineering, and the work is deeply technical, but the point of the work is fraud prevention for real clients. If you're going to feel frustrated that your pipeline work "isn't ML enough," this isn't the right seat.

• You need someone to hand you a task list every morning. The path from start to a deployed pipeline isn't always mapped out. The right person for this role is someone who asks sharp questions, proposes a plan, and starts moving

• You're uncomfortable with AI-assisted workflows. Generative tools are a baseline. You'll use it daily and you'll build tools on top of it. You’ll learn how to leverage things but the interest has to be there for this to work.

• You don't have legal authorization to work in Canada. We don't currently support visa or relocation for internship roles.

What makes this opportuntiy unique:

• Direct production impact from week one. Your code will run against real fraud signals serving live clients, not sandbox projects.

• ML and engineering interface. Work daily at the boundary between data science research and production engineering.

• AI-assisted by default. AI-assisted workflows are the team's baseline toolchain. You will both use and extend these patterns.

• Strong mentorship structure. Day-to-day technical mentorship plus weekly 1:1 with the team manager. Code review with senior engineers across the team.

• Conversion-track framing. Strong intern performance has historically led to full-time SWE / MLE conversion on this team.

Why is our salary range so large?

This role pays between $50,000 and ~$62,000 (annualized, prorated to your ~6-month placement). That's a wide band, and we'd rather explain it than pretend it isn't.

The range covers every technical intern we hire across three variables: whether this is your first ML internship or your third+, whether you're at the beginning of your Bachelor's or at the end of your Master's, and actually extends beyond that for more senior / PhD or MBA level candidates who come into the Early Talent Program.

TL;DR: A first-internship bachelor's student and a Master's student with two years of work experience are both encouraged to apply: they just won't be paid the same, and they shouldn't be.

Not sure if you qualify for this role? We encourage you to apply anyways. At GeoComply, Passion, Hunger and Drive, (aka PhD) count for more than years of experience or specific skills.

Our workplace is built on mutual respect and inclusion. We know that diversity of experience and thought has led to connection, innovation, and our company’s success.

We welcome applicants of all backgrounds, communities, experiences, beliefs, and identities.

Estimated Recruiting Timeline for this role:

Application Period: Late May -> Early June

Interview Period: Mid June -> Early July

Offers: Early July

Start Date: Early -> Mid July

End Date: December 2026 (6 month term)

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.