Top Python Jobs in Toronto

Responsible for software integration and quality for Cerebras AI platform, focusing on automation, debugging, and cross-team collaboration. Develops QA strategies and testing methodologies to ensure product quality.

Design, build, and optimize cloud-native lakehouse data pipelines and curated data products using Python and SQL. Own data modeling, performance tuning, observability, governance, and reusable platform patterns to support analytics, embedded reporting, operational workflows, and AI/ML datasets. Mentor engineers, lead reviews, and improve platform reliability, cost efficiency, and data quality.

Build and deploy generative ML features for Ideogram's app and API: fine-tune and ship models, design evaluations and datasets, run error analyses, monitor reliability, and collaborate with research, infra, product, and clients to meet quality, latency, and cost goals.

Provide operational support for a client through data collection, cleansing, modeling, reporting and visualization. Build automated standardized reports and surveys, interpret results, and make data-driven recommendations to support sales and marketing objectives.

Lead and build shared AI infrastructure and production-grade agents for a product analytics team. Establish repo standards, evaluation tooling, governance compliance, monitor production agents, close data pipeline gaps, and drive adoption across ~35 analysts and 60+ products while interfacing with cross-team AI and data platform partners.

Design, train, and deploy real-time computer vision and multimodal perception models for person detection, tracking, pose/gesture/emotion recognition, and scene understanding. Build data collection/labeling pipelines, optimize models for edge inference, integrate perception with behavior and firmware, debug failures, and evolve perception architecture from prototype to deployed robot systems.

Act as the bridge between business and technical teams to gather, validate, and document requirements; advise stakeholders on system solutions; optimize workflows; translate BRDs into technical specs; oversee vendor/data integrations; design test cases and coordinate UAT and training; and support project scoping, timeline estimation, and risk management.

Design, build, and operate secure, scalable AWS cloud solutions. Develop and maintain reusable IaC (CloudFormation/CDK), policy-as-code (Rego), serverless and container patterns, observability, and automation. Collaborate with security/compliance, support PoC-to-production implementations, and enable development teams through guidance, standards, and mentoring.

Lead pre-sales and solution design for enterprise AI, cloud, software and data engagements. Craft proposals, proofs-of-concept, and reference architectures; run workshops; align stakeholders; support delivery transition; collaborate with Sales, Marketing and Delivery to grow the AI practice and drive measurable client outcomes.

Lead and execute perception R&D and engineering for factory robots: define multi-year roadmap, architect perception pipelines (detection, segmentation, 6-DoF pose, scene understanding), drive sensor fusion and calibration, establish data/model workflows and deployment practices, integrate with planning/control, hire and mentor engineers, and evaluate research for production adoption.

Design, build, deploy, and operate an internal AI platform and AI-powered developer workflows. Implement CI/CD for models, prompts, and evaluations; integrate Bedrock/agent frameworks; ensure security, observability, reliability, and cost/version controls; respond to incidents and participate in on-call rotations; promote AI best practices and lead architecture discussions.

Design Verification Engineer responsible for verifying Tensix IP, building reusable SystemVerilog/Python testbenches and DV infrastructure, developing constrained-random and directed tests, closing coverage, debugging RTL/testbench/workload issues, and integrating CI/CD, simulation/emulation, and containerized regression systems. Collaborates with design, firmware, compiler, and architecture teams.

Provide L2 production support for data and client platforms in a 24x6 global follow-the-sun model: incident triage, escalation, SLA-driven communications, problem management, monitoring/automation (Grafana, ITRS Geneos), scripting-based automation (Shell/Python), SQL-based data investigations, change management, and mentoring SMEs while contributing to cloud migration and AI-assisted operations initiatives.

Design, build, and maintain integrations and automations between internal systems and third-party platforms (ERP/finance). Collaborate with stakeholders, use iPaaS tools and API standards, perform code reviews, participate in on-call rotation, and deliver well-tested integration projects on schedule.

Execute hydrogeological modelling and field programs: build conceptual site models and 3D groundwater models, prepare technical reports and proposals, design and analyze aquifer tests and monitoring, mentor junior staff, liaise with clients/regulators, and ensure successful project delivery (scope, schedule, budget, QA/QC).

As a Software Engineer in Data Infrastructure, you will build and maintain high-performance data storage systems for AI training, collaborate with experts, and optimize data processing.

Develop and improve multimodal representation learning frameworks (algorithms, models, data processing), run large-scale experiments, integrate novel solutions, stay current with research, and contribute to patents and publications.

Build scalable ML data pipelines for Waabi's autonomous driving platform. Design, optimize, and manage datasets and training processes while collaborating with scientists and engineers.

Contribute to the development of Waabi World, improve simulation scalability, and design orchestration software for autonomous vehicle simulations.

The Principal Platform Engineer will design and implement AI platforms, including distributed systems and orchestration frameworks, mentor engineers, and define best practices for LLM integration.

The Principal Platform Engineer will design and build scalable ML platforms, develop cloud-native infrastructure, and manage the model lifecycle in enterprise environments, focusing on MLOps capabilities and mentoring engineers.

The Senior Platform Engineer will build AI systems, develop APIs, support scalability, and integrate AI capabilities for financial services clients.

RAVL is seeking Senior ML Platform Engineers to build and scale machine learning platforms, develop ML pipelines, and support MLOps workflows in cloud environments.

Perform deep root-cause debugging across frontend-to-backend stacks, troubleshoot and read SDK source, support enterprise customers on architecture, and build agentic automation (LLM orchestration, MCP, internal APIs) to create deterministic autonomous support paths.

Design and develop high-performance distributed software for scalable AI training systems, focusing on data pipelines and system efficiency.