Salary Range: 50000 to 62000 (Currency: CAD) (Pay period: per-year-salary)

Location: Vancouver, BC (in person)

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

GeoComply'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 that will help us build and maintain these systems. You'll take validated detection logic and turn it into production batch and streaming pipelines on Databricks. You'll write the data processing, feature engineering, tests, and monitoring.

Our team runs on internal automation tools that handle deployment, signal investigation, monitoring, and ticket coordination. You'll design and extend these tools, turning one-off scripts into reusable skills that compound across the whole team.

You'll also spearhead the development of our internal “Governance Hub,” a system that tracks models, shipping checklists, and governance artifacts as models move through review and into production.

By the end of your internship, you'll have shipped code that runs against real fraud signals every day.

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)

You love working in Python
You're a degree in Computer Science, Software Engineering, Data Science, or have equivalent experience. A strong software engineering foundation matters more than ML coursework or prior experience.
Solid fundamentals: data structures, algorithms, Git, clean code habits.
Familiarity with at least one cloud data platform (Databricks, GCP, or AWS) and with databases (relational or NoSQL).
Comfortable working in modern AI-assisted development workflows (Claude Code or equivalent), or eager to learn.
This is a highly collaborative team, so student leadership experience or extra-curriculars outside of schooling is an asset.
Clear communicator: written and verbal across technical and non-technical teams.

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.

About GeoComply's Early Talent Program

Most Early Talent Programs or positions assign an intern a project or a manager and hope it works out. Ours is built differently, and we iterate on it every cohort based on what we learn.

You'll start with an onboarding Boot Camp: a week of product immersion where you learn what GeoComply actually does, why it matters, and how your work connects to the business.

By the end of the first week, you'll be asked to identify the biggest problem on your team and send a hypothesis directly to our co-founder.

From there, the structure is designed to make sure you're never invisible:

A dedicated buddy: a recent program alum whose job is to be the person you ask the questions you'd feel weird asking anyone else. They meet you before your first day, debrief you after your hardest weeks, pressure-test your showcase presentation, and support you every step of the way.

Direct access to senior leadership. You'll have a 1:1 with the co-founder during your first month, skip-level meetings with the Senior Leader for your team at six weeks, and a Showcase presentation to the whole company with your results from your internship at the end of your term.

A program that actually listens. Every cohort feeds back into the next one. The current program structure; Claude access on day one, onboarding bootcamp, skip-level meetings, and the buddy program, exists because previous interns told us what wasn't working and we changed it.

A conversion track. 8-10% of GeoComply's global headcount is made up of previous Early Talent Program Alumni. we see this program as our entry level talent pool, and rarely hire for entry level roles outside of it.

This isn't a program that treats interns as cheap labor or sandboxes them on side projects. You're selected from thousands of applicants, given production-grade work, held to a high bar, and supported with the structure to actually clear it.

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)

More about the ETP (Early Talent Program) at GeoComply.

Most companies see interns as cheap labour, temporary workforce or someone to delegate the tasks you don’t want to do. This as a huge missed opportunity.

GeoComply views our Early Talent Program as an opportunity to hand select and shape the growth of our future leaders. To equip them with the skills we want our future team leaders to have in five years, and our future executives to have in fifteen. We believe in developing our future pipeline of people as thoughtfully as we think about developing our products.

Here are the stats to back that up:

- GeoComply supports an average of 60 intern positions a year (globally)

- 8-10% of GeoComply’s current workforce were hired on as interns

- Each intern receives hundreds of hours of job specific training and professional development from their managers, mentors, and the Early Talent Program.

To check out our amazing benefits and learn more about the Early Talent Program at GeoComply, please visit: https://www.geocomply.com/careers/internship/

ML Engineer Intern

GeoComply

Compensation Overview

What You'll Actually Do: (Key Responsibilities)

Who we're looking for: (Hard Requirements)

Nice to haves:

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

Don't Apply If:

What makes this opportuntiy unique:

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