Top Python Jobs in Toronto

Join a talent network connecting founding full stack engineers with VC-backed startups. Responsibilities include designing and building front-end and back-end systems, developing APIs and cloud architectures, collaborating with founders on technical strategy, contributing to technical roadmaps, writing clean code, and establishing engineering best practices.

Join a talent network connecting Principal AI/ML Engineers with VC-backed startups. Responsibilities include architecting and deploying production ML/DL models, researching state-of-the-art methods (LLMs, transformers, RL), building scalable training/inference pipelines, implementing robust MLOps, optimizing model performance, and leading technical strategy and teams.

Join a talent network connecting principal front-end, back-end, and full-stack engineers with VC-backed startups. Responsibilities include driving technical architecture, building scalable full-stack applications, designing APIs and cloud-native services, optimizing performance and reliability, leading deployment and CI/CD best practices, and mentoring senior engineers while aligning technical decisions with product and business goals.

Join a talent network connecting founding AI engineers with VC-backed startups. Responsibilities include designing, building, and deploying ML/DL models, creating scalable data pipelines, optimizing real-time and batch model deployments, developing RAG and LLM systems, and aligning AI strategy with founders and product goals.

Join SignalFire's talent network to be considered for Staff Backend Engineer roles at early-stage startups. Responsibilities include designing, building, and maintaining high-performance backend services, APIs, and microservices; optimizing databases, caching, and event-driven systems; ensuring security, fault-tolerance, and cost efficiency; and establishing best practices for DevOps, CI/CD, and monitoring.

Join a SignalFire talent network to be matched with VC-backed startups seeking Senior/Staff Data Scientists. Responsibilities include experimental design and analysis, predictive and causal modeling, productionizing models, building metrics and dashboards, advising product and business teams, mentoring other data scientists, and shaping data strategy and tooling at fast-moving startups.

Partner with founders to build and scale engineering teams, define technical strategy and architecture, establish CI/CD and DevOps practices, oversee infrastructure and reliability, drive high-velocity delivery, and hire and develop top engineering talent at high-growth startups.

Forward Deployed Engineers partner with customers to design, build, and deploy production solutions and integrations, troubleshoot infrastructure and product issues, translate customer needs into scalable architectures, and inform product roadmaps while supporting pilots through production.

The Senior Product Manager will lead product strategy and execution, collaborating with cross-functional teams to enhance life sciences analytics products and meet customer needs.

Evaluate and label model-generated and human-written code outputs, debug and assess code/agent trajectories, prompt models for complex data science tasks, review visualizations, and report quality trends to improve model behavior.

Evaluate and label model-generated and human-written software engineering outputs for coding, debugging, and codebase-related tasks. Assess agent trajectories, prompt models for complex coding tasks, proofread and improve responses, and report quality and performance trends. Part-time contractor role contributing to model development.

Support stakeholders by profiling, mapping, and validating financial reconciliation datasets. Build complex SQL queries and Python workflows, and use Snowflake and Databricks to design, transform, and manage large-scale finance data for reporting, reconciliation, and analytics. Ensure data quality, perform root-cause analysis, and support governance and documentation.

The Senior Full Stack Developer will build intuitive financial products, develop data visualizations, and improve user experience. You'll work in cross-functional teams, driving innovation and ownership in product development.

Lead-platform principal engineer who codes, debugs, and sets reference implementations across AI-powered CX systems. Own platform architecture decisions, unblock squads by shipping production code, mentor engineers, and build high-capacity, event-driven distributed systems integrating messaging, multiple DB types, multi-cloud, and advanced AI (LLMs, RAG, agents).

Lead a squad as a player‑coach building event-driven, high-throughput, low-latency platform services and AI/ML pipelines. Implement Go services, connectors, and real-time WebSocket backends; own latency budgets, delivery, code quality, and weekly demos.

Lead day-to-day engineering for an AI-first SaaS platform, owning delivery, hiring, squad performance, architecture reviews, cross-team dependencies, and driving AI-assisted engineering practices to ship weekly demos at high quality.

Lead design and implementation of cloud-native infrastructure and DevOps practices for a new AI-driven life sciences platform. Build IaC, CI/CD pipelines, and automated deployments; manage and scale infrastructure with Ansible, Terraform, and Kubernetes; improve reliability, performance, and operational risk; mentor engineers and drive architectural best practices.

Lead applied AI projects from prototype to production, building and validating ML and GenAI models (LLMs, embeddings, RAG, agentic workflows) for malicious-behavior, anomaly, and fraud detection. Advise product and engineering teams, design experiments and evaluation pipelines, translate research into scalable APIs/workflows, and ensure data governance, privacy, and ethical practices.

Build and operate governed, reproducible AWS-based data pipelines and analytical products. Stand up managed AWS foundation, deliver top-priority data products, support product teams to follow architecture, and apply disciplined reliability and governance while using low-code only when appropriate.

Build, improve, and deploy fraud and loss detection models using supervised and unsupervised ML, causal inference, optimization, and experimentation. Partner with engineering and operations to productionize models, run experiments, and surface insights to shape fraud strategy and reduce financial crime.

Serve as the technical bridge between Irth's AIP platform and pipeline operators: lead discovery, map and migrate ILI/GIS/CP datasets, configure platform modules, validate and QA data, build ETL/SQL solutions, support API/GIS integrations, deliver training, and troubleshoot integrations while traveling to customer sites ~30–50%.

Design and build high-quality margin and collateral software solutions across the full development lifecycle. Collaborate with product and users, implement RESTful APIs, apply TDD and clean-code practices, and work in a fast-paced, global engineering environment using Java/React/Python and SQL.

Lead end-to-end analytics for business decisions using data insights and AI/ML. Collaborate with cross-functional teams to build scalable solutions and drive advanced analytics adoption, delivering actionable insights and compelling visualizations.

Lead development and deployment of machine learning and generative AI solutions (including prompt engineering, RAG, and LLM fine-tuning). Design experiments, preprocess large datasets, deploy models on Azure/Databricks, collaborate with engineers and stakeholders, and peer-review models and code to drive Group Functions analytics.

Lead mobile QA strategy for iOS and Android: design and scale automation frameworks (Appium, Espresso, XCUITest), integrate test suites into CI/CD, test on real devices via BrowserStack, perform API and exploratory/manual testing, analyze defects, and mentor junior QA engineers to ensure performance, stability, and excellent UX.