Senior Software Engineer (Global Security)

RBC•Toronto, ON

About The Position

RBC's Security Research & Innovation team is responsible for researching threats to the organization and its clients and employees and designing solutions and techniques for countering these threats. We are a team of experienced and enthusiastic security experts, researchers, developers, and innovators that are passionate about envisioning novel solutions to complex cyber security and digital crime problems. We take ideas through the full lifecycle of research, experimentation, development, and deployment to build solutions in areas where security vendors are one step behind. We leverage cutting edge cloud Devops, AI/ML, and security analysis technology to reduce risk to our organization, employees, and clients. We’re looking for a Senior Software Engineer with a passion for security and a builder’s mindset — someone who thrives at the intersection of cutting-edge Python development, cloud-native architecture, and applied AI. The right candidate combines deep engineering skills with security domain knowledge and is driven to create platforms that give defenders a decisive advantage over adversaries.

Requirements

5+ years of hands-on software engineering experience, with the majority in Python — including async patterns, packaging, and production-grade service development
Proven experience designing and deploying cloud-native applications on at least one major cloud provider (AWS, GCP, or Azure), including compute, storage, networking, and IAM
Solid understanding of RESTful API design and experience building backend services consumed by internal tools or analysts
Experience building and operating data pipelines at scale — ETL/ELT, streaming or batch, with tools such as Kafka, Airflow, Spark, or cloud-native equivalents
Practical experience integrating AI/ML capabilities into production systems — whether LLM APIs (OpenAI, Anthropic, etc.), model inference, or ML frameworks (scikit-learn, Pytorch)
Strong working knowledge of containerization (Docker) and orchestration (Kubernetes or equivalent)
Familiarity with security concepts — threat intelligence, attack frameworks (MITRE ATT&CK), log analysis, or security tooling — sufficient to engage meaningfully with security researchers
Demonstrated engineering discipline: version control (Git), CI/CD pipelines, automated testing, and code review practices
Ability to work independently on ambiguous problems and translate loose research requirements into well-scoped engineering deliverables

Nice To Haves

Experience building platforms or tooling specifically within a cybersecurity context (SOC, DFIR, Threat Intel, Red Team support)
Hands-on experience fine-tuning/prompt engineering for domain-specific LLM applications
Familiarity with OSINT techniques, dark web data sources, or digital crime investigation workflows
Experience with graph databases for relationship-based threat analysis
Knowledge of offensive and defensive security tooling
Contributions to open-source security tooling or research publications
Experience working in an agile environment embedded with non-engineering domain experts (researchers, analysts, investigators)

Responsibilities

Design, build, and iterate on a custom security research platform using modern Python (FastAPI, Pydantic) and cloud-native services
Architect and implement scalable data pipelines that ingest, enrich, and correlate threat intelligence from diverse sources including threat feeds, data lakes, and third-party security APIs
Develop and integrate AI/ML capabilities — including LLM-powered analysis, RAG pipelines, and fine-tuned models — to accelerate security research workflows and surface actionable intelligence
Use your creative mindset to build internal tooling and techniques to counter active cyber attacks
Leverage cloud compute to enable high-throughput, low-latency analysis of large-scale security datasets
Collaborate closely with security researchers and data scientists to prototype new detection and investigation capabilities
Maintain engineering best practices across the platform: CI/CD, automated testing, observability (logging, tracing, alerting), and secure software development lifecycle
Evaluate and integrate emerging AI frameworks, foundation models, and cloud services to continuously improve platform capabilities and research velocity

Benefits

bonuses
flexible benefits
competitive compensation
commissions
stock where applicable
Leaders who support your development through coaching and managing opportunities
Ability to make a difference and lasting impact
Work in a dynamic, collaborative, progressive, and high-performing team
Flexible work/life balance options
Opportunities to do challenging work
Opportunities to take on progressively greater accountabilities
Opportunities to building close relationships with clients