Top Python Jobs in Toronto

The Data Analyst will analyze large datasets, collaborate with stakeholders, develop data processes, and support data-driven decision-making to improve business efficiency.

Lead design, development, and maintenance of scalable full-stack applications using React, TypeScript, and Python. Define architecture, APIs, CI/CD, and distributed systems. Drive adoption of Claude Code and agentic AI workflows, establish prompt-engineering standards, and implement quality gates for AI-generated code.

Lead design and implementation of full‑stack, cloud‑native applications; analyze complex business and system processes; coach mid‑level developers; drive application architecture, coding standards, testing, and deployment; integrate Gen‑AI capabilities and ensure regulatory and policy compliance.

Productionize, optimize, and scale LLM and ML inference across multi-cloud and Kubernetes. Deploy GPU-based workloads, implement routing/failover for model services, reduce latency via quantization/pruning/distillation, profile and tune hardware utilization, and build observability, monitoring, and cloud-native guardrails for enterprise AI services.

Lead security and compliance architecture for CoCounsel, implementing SOC 2, ISO, HIPAA, and FedRAMP controls; build compliance-as-code, data protection (encryption, key management, tenant isolation), IAM, observability, and audit tooling; partner with security, legal, and engineering; mentor teams and ensure production-scale, provable compliance and incident readiness.

Lead design, build, and operation of cloud infrastructure, CI/CD pipelines, and platform tooling. Implement IaC (Terraform), automate deployments, troubleshoot live systems, maintain monitoring/logging, mentor engineers, set DevOps technical direction, and evaluate new tools.

Lead and grow a team of DBAs and data engineers to ensure reliable, scalable, and secure data infrastructure. Oversee heterogeneous databases, define cloud and containerization strategies, drive IaC and automation, support data pipelines and ETL/ELT, and implement high availability and disaster recovery practices.

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.

The Senior Data Engineer will scale the data ecosystem, construct data models, build systems for KPI tracking, and ensure data safety.

Build and own marketing measurement, attribution, experimentation, and analytics for performance, influencer, and lifecycle marketing. Responsibilities include creating dashboards, debugging attribution across advertising platforms and CRM systems, designing incrementality and A/B tests, analyzing CAC, ROAS, payback, cohorts, LTV, and channel efficiency, developing attribution taxonomies and instrumentation, and translating findings into recommendations for marketing, product, finance, and executive stakeholders.

Own the highest-level technical escalations, investigating complex bugs, data inconsistencies, API integrations, OAuth configurations, webhooks, permissions, and recovery scenarios. Reproduce issues, read and write code for debugging, analyze logs and API responses, and partner with Engineering through resolution. Support enterprise onboarding, maintain technical knowledge resources, identify recurring product issues, and participate in weekend and holiday on-call rotations.

Build scalable data pipelines, ELT processes, infrastructure, and dimensional data models supporting internal analytics and customer-facing data products. Implement data quality monitoring, auditing, alerting, warehouse optimization, and governed semantic layers for AI and LLM-enabled analytics. Partner with technical and business stakeholders, improve data processes using AI, and deliver reliable metrics from diverse data sources.

Partner with founders and leadership to turn internal data, market research, and competitive analysis into strategic decisions. Own analytics from raw data through recommendations, identify growth and margin opportunities, build financial and business cases, and launch initiatives that improve efficiency, unit economics, and profitability. Drive strategic projects end-to-end while measuring progress against business outcomes.

Partners with founders and leadership to turn data into strategic decisions affecting growth, margins, and profitability. Owns analytics from raw data through recommendations, conducts market and competitive analysis, builds business cases, and leads strategic initiatives end-to-end. The role identifies efficiency and monetization opportunities, develops financial models, measures business outcomes, and supports cross-functional decision-making.

Develop and verify firmware algorithms for DDR5/LPDDR5 memory calibration on FPGA IP cores. Design architecture per protocol specs, implement firmware, integrate with CAD tools, perform verification, hardware bring-up, product release support, and provide customer technical support.

Architect and implement robust bimanual manipulation algorithms and learning-based robot policies (diffusion, imitation, RL). Build end-to-end autonomy connecting perception to low-level control, write and maintain production Python/C++ code, and scale performance across a fleet in industrial environments.

Build end-to-end AI product features across frontend and backend: design LLM agent systems, streaming APIs (SSE), asynchronous AWS pipelines, document processing, and responsive React UIs. Collaborate with product, design, and core platform teams to iterate and ship production-ready, low-latency AI experiences.

Design, implement, and scale customer workflows on Feathery across pre-sales and post-sales. Own technical scoping, proofs-of-concept, onboarding, demos, deployments, and enablement. Provide product feedback, create technical content, and help build the solutions engineering function.

Design and build full-stack features for an LLM-powered coaching platform: architect backend services and APIs, integrate models, craft responsive front-end experiences, ensure enterprise-scale performance and security, drive CI/CD and observability, debug across the stack, and mentor engineers while collaborating with product and design.

The Performance Engineer - Inference will optimize model inference speed and throughput, debug low-level kernel performance, and develop tools to visualize performance data.

Lead post-silicon bring-up and optimization of wafer-scale engines: develop debug flows, HW-SW codesign optimizations, build large-scale test infrastructure, create self-checking metrics and instrumentation, and collaborate across silicon, performance, and software teams to improve production performance and release processes.

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.

Lead post-silicon bring-up and performance optimizations for Cerebras Wafer Scale Engines. Develop/debug production-ready flows, instrumentation, and self-checking metrics. Build infrastructure for large-scale silicon workload testing, collaborate with silicon architects, performance and software teams, and streamline CI/CD and release workflows.

Support client operations through data collection, cleansing, transformation, modeling and reporting. Build and automate standardized reports, design surveys and market tests, use BI tools to visualize insights, and make data-driven recommendations to support sales and marketing objectives.

As an AI Product Engineer at Puzzle Cats, you will develop and enhance AI-driven mobile game experiences, collaborating across teams to rapidly prototype, ship, and iterate on features that can quickly reach millions of players.