Junior Software Engineer

Trulioo•San Diego, CA

2d•Hybrid

About The Position

Are you ready to embark on a career that truly affects people around the world? Trulioo invites you to be a catalyst for change in the dynamic realm of digital identity verification. As the global front-runner in our industry, we are redefining how businesses grow, innovate and comply online. Picture yourself at the forefront of innovation, contributing to our award-winning platform that enables organizations worldwide to quickly onboard customers, optimize costs and combat fraud. Fueled by Silicon Valley support, Trulioo stands as the trusted platform that can verify more than 5 billion people and 700 million business entities spanning 195 countries. But Trulioo is more than a tech company. We are a united force of dedicated experts committed to establishing trust online - and we’re proud to be recognized as a BC Top Employer for the second consecutive year, reflecting our commitment to an inclusive, collaborative, and people-first workplace. Headquartered in Vancouver and with strategic hubs in San Diego and Dublin, we foster a culture of collaboration and open communication. Our offices support a hybrid model and staff typically work three days per week at a hub location. Join us where excitement meets innovation and contribute to a world where trust and technology unite.

Requirements

1-2 years of experience working in a software engineering or development environment.
Master's degree in Computer Science, Data Science, Engineering, or a closely related field.
Solid foundation in Python and/or another backend language, with the ability to write clean, readable, testable code.
Understanding of SQL and/or NoSQL databases, and basic API design (REST at minimum).
Exposure to cloud infrastructure (AWS preferred) — coursework, internship, or personal project experience is fine.
A self-starter mindset — comfortable with ambiguity, asking good questions, and figuring things out rather than waiting for a fully specified task.
Genuinely excited about working with the newest AI tooling — you've already been experimenting with it because you wanted to, not because a class required it.
Driven to actually ship usable products: you'd rather get a real project working end-to-end than perfect just one piece of it.
Eager to learn how production systems really work — testing, deployment, monitoring, on-call support and troubleshooting— beyond what a class or internship typically covers.
A good collaborator who communicates clearly and is comfortable working alongside more senior engineers.
Comfortable using AI coding assistants (e.g., Claude Code, GitHub Copilot, Cursor) as part of how you write code, and interested in getting better at it.
Have built at least one project — for coursework, a hackathon, research, or on your own — involving an LLM doing more than single-shot Q&A: calling tools/functions, chaining steps, using retrieval, or coordinating multiple steps toward a goal.
Genuinely interested in the practical side of AI engineering: why these systems behave unpredictably, and what it takes to make them reliable enough to ship.

Nice To Haves

Some exposure to data pipelines or data processing (e.g., via coursework, internships, or projects using tools like PySpark, Airflow, or similar) is a plus, not a requirement.
Coursework or projects touching orchestration tools (Apache Airflow, Dagster), OpenSearch/Elasticsearch, or BigData tools (Spark, Presto/Trino, Hive).
Familiarity with TypeScript/React/Node.js for full-stack project work.
Exposure to infrastructure-as-code (Terraform, AWS CDK).
Interest or coursework in identity verification, fraud, security, or fintech.
Exposure to LLM APIs (Anthropic, OpenAI, Google Vertex AI/Gemini) or agent frameworks (LangGraph, LangChain, AutoGen, or similar) is a strong plus, even at a project or coursework level — deep production expertise is not expected.

Responsibilities

Help take various Discovery Agent prototypes (e.g. Vertex AI-based) through the SDLC — contributing to design, implementation, testing, and deployment (via API and Agentic access).
Help harden POC-quality agent code toward production quality: error handling, observability, testing, and basic cost/latency awareness.
Help build test cases and evaluation checks for agent behavior — an important and different skill from testing traditional deterministic code.
Support data ingestion and pipeline work feeding the agent, using AWS/BigData tooling.
Work with Product team and senior engineers to turn business requirements into shipped features.
Learn and contribute to production support practices: monitoring, alerting, and incident response for an AI-driven service.