Top Python Jobs in Toronto

Own QA for web and mobile releases: write and run manual and automated tests (web, mobile, API), build test frameworks, improve CI, track issues, mentor teammates, and influence requirements and quality processes.

Lead ownership, harden, and operate an AWS-native backend/platform: build APIs, event pipelines, data services, compliance features (audit logging, encryption, data lifecycle), deployment automation, testing/acceptance tooling, and shape architecture and engineering hiring.

Lead client-facing integrations and implementations for an AI-native, compliance-focused travel platform. Design, build, and operate backend services, event pipelines, and data services on AWS. Ensure auditability, encryption/key management, consent and data lifecycle handling, and translate client feedback into product improvements while shaping architecture and early hiring.

Build, maintain and improve Maya-centered production tools and pipeline integrations. Develop Python-based plugins, UIs, and automation; integrate Maya with asset management and render farm systems; troubleshoot artist-facing issues; ensure cross-platform compatibility; participate in code reviews, deployments and pipeline architecture improvements.

Design, implement, test, and maintain high-quality Python backend services and REST APIs. Ensure security via SCA, participate in code reviews, own technical designs, estimate and report status, document requirements, and collaborate with cross-functional teams to deliver scalable, production-grade systems.

Own end-to-end software architecture for humanoid robot applications, design modular, production-ready platforms integrating robotics frameworks, AI, cloud/edge, and hardware constraints; lead technical decisions, guide POCs to deployments, and mentor a small engineering team.

Lead growth and player behavior analytics across sportsbook, casino, and poker products. Partner with Revenue Ops, Marketing, Product, and VIP to run retention, bonus optimization, segmentation, funnel/cohort analyses, and A/B tests. Build predictive models and apply ML/AI tooling to generate actionable insights and recommendations that drive engagement, personalization, and ROI.

Run discovery workshops, design AI-enabled customer experience solutions, build demos and prototypes, validate use cases, and support transitions to production. Advise on integrations, governance, and AI trade-offs while collaborating with sales, product, and professional services to drive customer outcomes.

Build and maintain full-stack features spanning frontend UIs and backend services. Design, develop, test, deploy, and support code; participate in architecture discussions, code reviews, and collaborate with Product and QA. Use AI-assisted coding tools, identify technical debt, and contribute to scalable, reliable solutions.

Serve as a trusted advisor delivering accounting advisory and finance transformation services. Support month-end close, reconciliations, journal entries, SEC and regulatory reporting, budgeting/forecasting, internal controls, ERP implementations, AI-enabled process improvements, transaction accounting support, and external audits while communicating findings and recommendations to clients.

Serve as a client-facing accounting advisory leader delivering controllership and operational accounting support, financial reporting and SEC filing assistance, month-end close optimization, ERP/cloud finance implementations, automation/AI enablement, and team leadership across PE-backed and growth-stage companies.

Design, develop, refactor, and migrate legacy ASP.NET and backend applications using Python and C#. Build microservices, containerize with Docker/Kubernetes (EKS), deploy to AWS, ensure application security, and optimize SQL database performance while collaborating with cross-functional teams.

Support Financial Planner business analytics: set targets, produce performance and compensation analysis, design scorecards, build dashboards and recurring reports, integrate and validate data, automate reporting, and deliver executive-ready insights and ad-hoc deep dives to inform performance management and strategic decisions.

Work with founders to solve cross-functional problems (ops, GTM, partnerships). Build and optimize scalable processes, set and track operational KPIs, lead end-to-end projects, and analyze data to provide insights that improve product and customer experience.

Design, build, and maintain production data pipelines using Airflow/Cloud Composer and BigQuery; define data models and optimize queries; deliver custom solutions from POC to production; collaborate with Data, Product, and Customer Success; deploy dashboards/data apps; continuously improve data infrastructure and leverage AI tools to accelerate development.

Own quality and reliability of the Cerebras Inference Platform by building test infrastructure and automation, validating Kubernetes and hardware deployments, debugging distributed systems and networking, developing testbeds for performance and scalability, and partnering with platform engineers to ensure production-ready releases.

Provide technical and operational application support across the full lifecycle, acting as liaison between users, developers, and vendors. Diagnose and resolve incidents, participate in releases and post-release validation, analyze risks/security gaps, drive support process improvements, maintain documentation, and support disaster recovery and weekend/shift rotations to ensure application stability and service effectiveness.

Lead the MLOps team focused on AWS cloud services and machine learning operations, design production-grade capabilities, and mentor other engineers.

Own paid and organic marketing end-to-end across multiple channels and countries. Run Meta and Google Ads, launch new channels (TikTok/LinkedIn/X), maintain brand consistency, automate repetitive workflows through an in-house AI-assisted platform, extend ads-API launch engines and guardrails, and measure funnel performance with data-driven attribution and BigQuery-backed analytics.

Design, build, and deploy scalable, secure data solutions on-premise and in Azure. Collaborate with engineering, security, and ops to implement modern data practices (Databricks, Snowflake, Python, SQL), produce solution designs, support project delivery, and promote DevSecOps and cloud best practices.

Join a VC-backed startup network connecting Director/VP-level AI/ML leaders to portfolio companies. Lead AI strategy, build and scale ML teams, deploy and improve production ML systems, set model quality and responsible AI standards, partner with product and engineering, oversee data and MLOps, evaluate models and tooling, and communicate strategy to executives and boards.

Join a talent network connecting founding backend engineers with VC-backed startups. Responsibilities include designing and maintaining scalable backend systems, building high-performance APIs and microservices, optimizing databases and data pipelines, defining technical strategy with founders, and establishing best practices for security, DevOps, and cost-efficient cloud infrastructure.

Join a talent network connecting senior/staff applied AI scientists to VC-backed startups. Responsibilities include researching and developing ML methods, fine-tuning foundation models, designing experiments and evaluation frameworks, building prototypes, collaborating with engineering/product to productionize models, improving model robustness and efficiency, curating datasets, investigating failures, mentoring peers, and communicating results to stakeholders.

Join a talent network connecting Staff Full Stack Engineers to VC-backed startups. Build and scale full-stack web applications, design APIs and microservices, optimize for performance, scalability and security, collaborate with product and design, and establish best practices for code quality, testing, and CI/CD across cloud-based architectures.

Join a talent network connecting data engineers with VC-backed startups. Build scalable batch and real-time pipelines, data models, warehouses/lakehouses, ensure data quality, support ML pipelines, integrate diverse data sources, and define data architecture and governance.