the company is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. People who move quick, think critically, act like owners, and care deeply about solving customer problems with precision. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.

The Agentic AI Foundations team is building the core platform, systems, and primitives that enable the company to transition from traditional software workflows to agent-native operations. As a Software Engineer II on the team, you will help design, build, and harden a secure, evaluable, vendor-agnostic agent platform that teams across the company can build on, working alongside senior and staff engineers who set the architectural direction. You’ll bring strong foundational knowledge of LLMs, agentic AI, and GPU/model serving through academic, research, professional, open-source, or other relevant experience, and grow into greater ownership as you build alongside senior engineers on the team.

Build components of a vendor-agnostic agent platform — including orchestration, tool use, memory, and runtime systems — under the guidance of senior engineers on the team. Implement evaluation and reliability tooling, including metrics, harnesses, and pipelines, to measure and improve agent performance, robustness, and safety in production. Help implement safety and governance controls, including guardrails, policy enforcement, and human-in-the-loop review mechanisms.

Build data grounding, retrieval, and memory components that keep agents accurate, context-aware, and aligned with the company’s domain knowledge and policies.



Apply and help refine documented best practices and design patterns for secure, observable, and scalable agent systems. Bring strong foundational knowledge of LLMs, GPU computing, and model serving to technical discussions and implementation decisions.

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Machine Learning/AI, or a related field from top tier institutions, or equivalent practical experience demonstrating strong foundations in computer science and machine learning. ~2+ years of professional software engineering experience, with demonstrated experience in distributed systems, backend platforms, infrastructure, or comparable technical environments. ~ Very strong foundational knowledge of large language models and agentic AI systems, including architectures, prompting and orchestration patterns, tool use, and evaluation approaches. ~ Strong foundational understanding of GPU computing and model-serving infrastructure, such as CUDA, vLLM, Ollama, LLMLite, TensorRT-LLM, Triton Inference Server, or similar technologies, including the performance and cost trade-offs associated with serving LLMs at scale. ~ Solid grounding in distributed systems fundamentals, including concurrency, fault tolerance, observability, and performance. ~ Proficiency in at least one modern backend programming language and ecosystem, such as Java, Go, Python, or similar, with comfort working with cloud-native infrastructure, APIs,



and data services. ~ Ability to work productively in ambiguous, early-stage problem spaces with guidance from senior engineers, translating direction into working software. ~ A track record of strong technical performance demonstrated through professional impact, research, challenging technical projects, open-source contributions, internships, or other relevant work. ~ Strong collaboration and communication skills, with comfort working alongside cross-functional partners such as product, data science, platform, and security.

Experience with multi-agent systems, workflow orchestration, or distributed coordination frameworks through professional work, research, coursework, or technical projects.

Experience building or using agent platforms — such as orchestration frameworks, tool registries, or memory systems — or LLM routing, caching, or fine-tuning pipelines through professional work, research, internships, open-source contributions, or personal projects. Exposure to evaluation frameworks, experimentation platforms, or ML systems, such as offline/online evaluations, A/B testing, or agent and model benchmarking.

Experience with AI safety, security, or policy systems — including guardrails, policy engines, content filters, or responsible AI frameworks — through professional work, research, coursework, or technical projects.

Experience with retrieval systems, knowledge graphs, or data platforms used to ground LLMs and agents in enterprise contexts. Demonstrated depth in ML systems or LLM infrastructure through professional impact, research, publications, technical projects, competition results, open-source contributions, or comparable experience. #

📌 Software Engineer II — Agentic AI Foundations (Toronto)
🏢 United States Digital Space
📍 Toronto