Top Software Engineer Jobs in Toronto

Design, build, and scale resilient distributed payment systems handling 1,000+ transactions per second. Collaborate with Product, Revenue Operations, Cloud Platform, and Security teams to shape product requirements and roadmaps. Lead AI-augmented software delivery, code reviews, testing, observability, performance optimization, incident response, on-call rotations, and root cause analysis while maintaining strict security, reliability, and low-latency standards.

Lead design and delivery of reliability projects for Coinbase's platform: secure service configuration and secrets management, improve canary-based deployments, partner with core services to increase scalability and reduce incidents, drive reliability best practices, and participate in on-call rotations.

Lead architecture and implementation of Sentry's Issue Workflow platform, solving performance and scalability challenges, driving technical strategy, building real-time pipelines and search systems, and mentoring engineering teammates to improve product quality and user experience.

Lead development of Elasticsearch vector similarity search capabilities, including designing algorithms and data structures, implementing new features, fixing complex bugs, and improving search relevance, performance, and code quality. Collaborate autonomously with a globally distributed engineering team and open-source community, contribute to Lucene, guide cross-functional initiatives, review code, and use modern Java to build scalable search software.

Build and operate full-stack services for non-human identity governance across cloud and on-premises environments. Responsibilities include developing APIs, backend services, data pipelines, and occasional UI features; owning features through production; participating in design and code reviews; resolving reliability issues; applying secure development, testing, and observability practices; and mentoring engineers.

Lead development of Elasticsearch vector similarity search capabilities, including designing algorithms and data structures, implementing features, fixing bugs, improving performance and relevance, and contributing to Lucene. Collaborate asynchronously with globally distributed engineers and open-source contributors, provide technical leadership, review code, and guide cross-functional initiatives. Develop modern Java software and serve as the subject-matter expert for vector search.

Lead design and delivery of LLM-powered agent and retrieval systems: build backend retrieval pipelines, orchestration and evaluation frameworks, optimize ranking and latency, integrate vector DBs and cloud infrastructure, collaborate with product and data teams, and drive production readiness for search and agentic workflows.

Develop and maintain applications in Infor CloudSuite Distribution, integrate third-party apps, and design full-stack web applications while collaborating with stakeholders and improving development processes.

Lead Elasticsearch search relevance and vector search initiatives. Design and implement search features, algorithms, and data structures; improve speed, relevance, and scalability; resolve complex bugs; and contribute to Lucene. Collaborate asynchronously with globally distributed engineers and open-source contributors, review code, guide technical direction, and write modern Java. Apply vector similarity techniques and algorithms such as HNSW or IVF on search platforms at scale.

Design, build, and maintain full-stack features across frontend (TypeScript/React) and backend (JVM language or Python). Mentor engineers, participate in code reviews and pair-programming, deploy generative AI infrastructure, and ensure quality via unit testing and CI in an agile, remote environment.

Senior full-stack engineer responsible for designing, building, and owning end-to-end features across frontend (TypeScript/React) and backend (JVM languages or Python). Mentor junior engineers, drive best practices (unit testing, CI), deploy generative AI infrastructure, work with databases and cloud/distributed systems, collaborate in cross-functional teams, and contribute to product excellence in a remote, agile environment.

Lead development of Elasticsearch search and vector-search capabilities, including designing algorithms and data structures, implementing new features, improving relevance and performance, fixing complex bugs, and contributing to Lucene. Collaborate autonomously with globally distributed engineers and open-source contributors, guide technical initiatives, review code, and use modern Java to build scalable search functionality.

Build and operate production systems for agentic development experiences, including agent execution, tool use, context management, streaming, integrations, and long-running workflows. Extend provider-agnostic model architecture and connect agents with workspaces, tools, and developer infrastructure. Own projects through rollout, collaborate with Product and Design, participate in reviews, and improve system reliability, performance, and operability.

Design, build, deploy, and optimize production-grade AI systems, including LLM applications, autonomous agents, multi-agent workflows, machine-learning models, data and inference pipelines, and evaluation frameworks. Develop secure, scalable AI services using retrieval-augmented generation, tool calling, orchestration, and model fine-tuning. Monitor reliability, performance, safety, cost, and business outcomes while applying governance, observability, and responsible AI practices.

Design, build, and maintain backend systems and microservices using C#/.NET (NET 8+). Develop RESTful APIs, data access layers, and Azure-deployed services. Implement containerization (Docker/Kubernetes), CI/CD, automated tests, and follow SDLC and Agile practices while participating in code reviews.

Design and deliver scalable backend services for Veeva Ostro's SaaS platform. Architect high-performance APIs, optimize application and database performance, collaborate cross-functionally to ship features, champion code quality and testing, evaluate backend technologies, and troubleshoot live system issues to maximize uptime.

Architect, develop, integrate, test, debug, and optimize embedded software for diverse hardware platforms using RTOS, bare-metal, Zephyr, and Linux environments. Lead software architecture, system validation, test automation, and multi-threaded application development while collaborating with hardware and software teams. Define test strategies, analyze defects, document designs, conduct code reviews, mentor engineers, optimize performance and power usage, and contribute to open-source projects.

As a Senior Backend Software Engineer, you will lead on architectural decisions, develop backend services and APIs, and handle scalable architecture for millions of users.

Build and ship full‑stack product features for a browser-based CAD application: own front-end architecture and UX, maintain backend services (APIs, Postgres on GCP), write well-tested high-performance code (including C++ core), and lead customer-facing technical engagements to turn feedback into product improvements.

As a Fullstack Software Engineer, you'll develop critical applications for Hadrian's FactoryOS, collaborating with teams to enhance manufacturing processes.

As a Software Engineer, you will collaborate in small teams, transform legacy systems, apply modern engineering practices, and continuously learn and grow.

Develop and optimize distributed systems for syncing customer data, focus on scalability, reliability, and performance for global clients.

Build and operate Valence's shared platform primitives (auth, routing, LLM gateway, deployment, observability, SLOs) to enable safe, scalable, and compliant delivery of AI-first products. Implement CI/CD gates, progressive delivery, rollback automation, security guardrails, region-aware routing, and developer-facing templates and tooling.

Design, build, and productionize LLM-based conversational and AI systems for enterprise coaching. Integrate structured and unstructured data, prototype end-to-end services, improve model behavior and observability, and collaborate with product and engineering to deploy scalable, reliable AI-driven coaching workflows.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly detection, visualization and analysis tools, and frameworks for failure classification and regression detection. Extend compilers, runtimes, and instrumentation for advanced profiling. Improve bring-up and low-level debug workflows, partner cross-functionally across hardware, firmware, compiler and runtime teams, lead high-impact initiatives, and support incident response and long-term corrective actions.