Top Software Engineer Jobs in Toronto

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

The ML Engineer will integrate model architectures, optimize deployment workflows, maintain CI/CD pipelines, and ensure reliability of inference services across large-scale systems.

The role involves using AI tools to rapidly develop software, working across product and engineering teams, and owning software projects end-to-end.

This role requires shaping technical direction, leveraging AI tools for production systems, and making crucial decisions to align product intent with engineering realities.

Design and build the Neki control plane to provision, scale, and heal sharded PostgreSQL clusters. Implement high-availability, disaster recovery, backup/restore, and migration tooling. Operate distributed, fault-tolerant services on Kubernetes, participate in on-call rotations, and work with customers to translate workloads into platform improvements.

You will design and implement IAM systems, own authentication and authorization, and ensure secure-by-default principles across Hadrian's API platform.

Serve as Braze technical and product SME; design and maintain integrations, player data models, pipelines, and migrations; partner with CRM/Marketing/Product to deliver personalised engagement; monitor performance and deliverability; ensure security, privacy, consent, and compliance; document architecture and support incident response for marketing systems.

Lead end-to-end technical implementations of Conductor for healthcare clients: build integrations and agentic workflows, write backend and frontend code, run demos and prototypes to accelerate sales, diagnose and resolve production issues, document runbooks, and translate deployment learnings into product improvements. Serve as primary technical contact through kickoff, go-live, and post-launch support.

Own architecture, design, development, deployment, and operations of provider integrations and full-stack features. Build scalable, reliable integrations between provider systems and PocketHealth, improve provider workflows, automate processes, and maintain production services with strong user empathy and quality focus.

The role involves building AI inference systems, optimizing GPU performance, and developing benchmarking methodologies for large-scale deployments.

Design, build, and scale a big-data platform and microservices for event-driven, time-series, and analytical workloads. Lead architecture for streaming, processing, and storage (performance, cost, fault tolerance), develop REST APIs, integrate messaging and scalable datastores, ensure security/compliance, and champion AI-assisted developer tooling.

Lead full‑stack development of customer‑facing features using React/TypeScript and Python, design and deliver GenAI/LLM integrations (agentic workflows, MCP/A2A), set technical standards, collaborate with product/design/platform, drive execution, and mentor engineers to improve code quality and system reliability.

Lead and improve assembly subsystem quality by analyzing warranty and EOL/CAL data, running control-plan and process audits, directing SPC activities, driving corrective actions (8D), supporting launch readiness, and leading lean/variability-reduction initiatives to ensure manufacturing process capability and compliance with Ford Production System and ISO 14001.

Lead applications systems analysis and development, design standards, and implementation. Partner with management to define enhancements, resolve complex issues, coach mid-level developers, and drive compliance and risk-aware decisions.

Lead architecture and delivery of an AI-ready data platform: define enterprise data models, medallion/lakehouse patterns, data governance, observability, secure interoperability, and mentor teams to build production-ready AI data products.

Lead design and delivery of an AI-ready, AWS-native enterprise data platform and lakehouse. Define architecture patterns, modeling standards, ingestion/CDC, observability, governance, and secure interoperability. Mentor teams, contribute hands-on to platform modernization, enable vector/RAG AI workflows, and provide reference implementations to drive consistent, scalable data products and analytics.

Design and prototype scalable microservice solutions, drive features through the Agile SDLC, improve CI/CD and production reliability, mentor developers, participate in technical design discussions, and integrate AI-assisted development into engineering workflows.

Architect and build core distributed systems for high-throughput real-time and batch data ingestion, processing, and storage. Ensure fault-tolerance, scalability, and strong query performance for time-series and spatial data. Develop automated tooling for data quality and lineage, collaborate on reusable abstractions, and write production-ready code (Java/Python).

Senior Software Engineer to design and implement C++/Python software, accelerate workloads with CUDA, develop and maintain Linux kernel modules and system services, optimize system and GPU performance, manage Linux systems, mentor junior engineers, and integrate AI technologies across products.

As a Senior Software Engineer, you will build and operate Upbound Spaces, resolve issues in SaaS environments, develop features, support project lifecycles, manage Kubernetes services, and enhance operational tools.

The role involves architecting AI solutions for compliance, ensuring regulatory adherence, automating processes, and enhancing team capabilities through technology and training.

Design, build, and operate scalable backend services for courier logistics. Own end-to-end systems including data modeling, event-driven flows, deployments, observability, and incident response. Mentor peers, drive architecture for distributed systems, and collaborate across product, mobile, QA, and data teams to deliver reliable backend features at scale.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from ad platforms, order systems, and real-time events. Ensure reliability, observability, and maintainability across event-driven and batch architectures, implement authentication flows, optimize data workflows in a cloud-native environment, and collaborate with product, infrastructure, and data teams to onboard new sources.

Build and scale core research infrastructure and agentic systems across backend, frontend, and model integrations. Own reliability, observability, and performance; ship research-proven features to production rapidly and develop shared tools and SDKs to accelerate Research, QA, and Engineering.

Lead design and delivery of self-service platform capabilities: provisioning, deployment, observability, and multi-region networking. Drive RFCs and cross-team alignment, build APIs (primarily in Go), implement Terraform/GitOps flows, improve EKS reliability and Grafana-based SLOs, and reduce operational toil via safe, auditable automation. Own on-call rotation and platform adoption outcomes.