Tech Lead of Software Engineering in Toronto

As part of the Platform Engineering team, you will help build DataVisor’s next-generation machine learning platform, combining our proprietary unsupervised machine learning technology with supervised machine learning algorithms to power real-time fraud detection at scale. As fraud attacks become increasingly sophisticated, real-time detection is more critical than ever. Our platform team is responsible for designing and developing the architecture that makes scalable, real-time detection possible, including streaming systems, storage layers, and training pipelines that support our core detection capabilities.

We are looking for a creative, hands-on engineering leader to help expand our streaming and database systems, improve our core detection algorithms, and automate the end-to-end training process. This role is ideal for someone who enjoys solving complex distributed systems problems, mentoring engineers, and driving technical execution in a high-impact environment. Join us as we continue to push the boundaries of fraud detection, machine learning infrastructure, and large-scale data processing.

• Own the technical direction of the real-time detection platform, including streaming, storage, and the training pipelines that support it.
• Translate product and engineering roadmaps into clear technical plans, milestones, and execution priorities.
• Proactively identify technical risks, dependencies, and trade-offs before they impact delivery.
• Lead design and architecture reviews, make technical trade-off decisions, and document the reasoning behind key decisions.
• Stay hands-on by coding, reviewing code, debugging issues, and supporting the team during production incidents.
• Mentor engineers and raise the bar for system design, code quality, operational excellence, and technical execution.
• Own the operational health of the platform, including alert quality, on-call load, incident follow-through, and root-cause prevention.
• Partner directly with Product, TAM, and customer-facing teams on customer-impacting issues, ensuring clear impact assessment, prioritization, ownership, and next steps.
• Define how the team uses AI agents and AI-assisted tools in engineering workflows, including verification standards and safe usage practices.
• Build, evaluate, and improve LLM- and agent-assisted tools for engineering and operations use cases, such as triage, root-cause analysis, alert summarization, and evaluation harnesses.

Benefits:

• Stock Options
• Commuter Passes
• Paid Time Off
• Catered Lunch and Dinner
• Team and Company Outings

Qualifications:

• Demonstrated use of AI coding tools such as Claude Code, Cursor, GitHub Copilot, or similar tools in real production work.
• Strong knowledge of computer systems, relational databases, and SQL.
• Ability to set team-level standards for AI-assisted engineering, including how tools are used, how outputs are verified, and when AI-generated suggestions should be rejected.
• Bachelor’s degree in Computer Science or a related field is required.
• Experience with the Spring Framework.
• Experience designing, building, shipping, and operating distributed real-time systems at scale.
• Proven ability to lead technical outcomes across a team, including work you did not personally implement.
• Experience building and optimizing multithreaded and concurrent applications.
• 2+ years of technical leadership experience as a tech lead, staff engineer, engineering manager, or similar role.
• Hands-on experience with Cassandra, Yugabyte, Flink, Spark, or Kafka.
• Strong verification discipline, with the ability to validate model outputs against source code, logs, documentation, and production behavior.
• 8+ years of software development experience.
• Deep production experience with Java, along with working proficiency in Python and Shell scripting.
• Experience in fraud, risk, payments, financial services, or another domain where false negatives carry significant business or customer impact.
• Experience owning ML platforms or large-scale training pipelines.
• Experience with Kubernetes.
• Experience writing evaluations or regression tests for non-deterministic systems.
• Experience building with LLM APIs, agent frameworks, tool calling, RAG, or MCP.
• Experience hiring, managing, or mentoring engineers.
• Experience with test-driven development.