Top Python Jobs in Toronto

Founding technical role to design, build, and deploy internal AI tools and agents using LLMs and retrieval systems. Partner with investors and the data team to accelerate sourcing, diligence, portfolio insights, and define CRV's AI strategy through rapid prototyping and production-grade systems.

Senior Geophysicist responsible for compiling, processing, and interpreting geophysical data to support AI-driven drill targeting. Collaborates with data scientists to integrate geophysical insights into predictive models, troubleshoots data issues, and contributes to methodology development and cross-disciplinary projects to improve exploration decision-making.

The AI Strategy Consultant will drive marketing strategy using AI, work with teams to implement solutions, and enhance customer outcomes.

Lead the quality engineering efforts for Replicant's AI platform by defining standards, improving testing strategies, and mentoring a QA team to ensure high-quality outputs across software systems.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Design and implement advanced generative AI models, optimize deployment in production, collaborate with teams, and stay updated on AI advancements.

Develop and maintain the Keycard Agent SDK, ensuring compatibility with various languages and frameworks, while engaging with the developer community to enhance agent applications.

As a Full Stack Engineer, you'll own the development of features from design to production, collaborate with various teams, and continuously improve the platform by solving technical challenges.

As a Senior Software Engineer, you will lead your team in delivering features according to specifications, ensuring scalable technical decisions, and mentoring team members.

As a Growth Engineer, you will integrate Ritual's AI infrastructure with partner applications, supporting integration lifecycle and developing documentation for APIs and SDKs.

Lead and develop a remote team of Python engineers, ensuring their growth and satisfaction while driving high-quality software delivery.

Design, develop, and maintain backend services for the Leap quantum-classical hybrid cloud platform. Collaborate with product owners and architects across the SDLC to deliver features, troubleshoot and tune production systems, write clean maintainable code, participate in code reviews and on-call rotation, and mentor junior engineers to improve engineering practices and platform scalability.

Design, build, and scale cloud lakehouse data products (Databricks/Snowflake) from ingestion to analytics-ready datasets using medallion architecture. Develop reusable Python frameworks, Spark pipelines, and dbt/SQL transformations. Implement data modeling, governance, observability, validation, and SLA monitoring. Troubleshoot production issues, adopt AI to accelerate engineering, and provide technical leadership, code reviews, architecture decisions, and mentoring for global engineering teams.

Own and operate backend and data services for a cybersecurity platform: design and run high-volume streaming ingestion, ETL/ELT pipelines, lakehouse/warehouse modeling, APIs, and observability. Mentor engineers, contribute across TypeScript/Python/Go microservices on Kubernetes, ensure performance/scale/security, and evaluate AI tools to accelerate development.

Lead architecture and delivery of AI-native Amazon Connect contact center solutions: design multi-agent conversational AI, implement contact flows and serverless backends, author IaC, build analytics and observability, mentor engineers, and manage risk and client engagements for production contact centers.

Lead insider risk and data loss investigations from triage to closure, operate and tune DLP across environments, build detections and workflows, partner with HR/Legal/Compliance/IT, mature insider risk program, support audits, and mentor others.

Own end-to-end design, build, deploy, and iterate on production AI systems that improve lead quality, reduce refunds, and automate workflows. Ship customer-facing AI features, agentic workflows, retrieval and context layers, reliable event-driven automations, evaluation datasets and monitoring, and reusable components while connecting technical decisions to business KPIs.

Build and optimize low-latency on-device and distributed GPU inference pipelines. Implement and tune low-level CUDA/Triton kernels, optimize for throughput and latency, and develop monitoring/debugging tools to ensure reliable, deterministic inference in robotics and cluster environments.

Hands-on field engineer who keeps robot data collection running: stand up and maintain stations, triage and resolve hardware/software/data issues on customer and partner sites, bring up new tasks and sites, and feed operational insights back to model, platform, and hardware teams.

Design and maintain foundational platform frameworks and internal tooling to accelerate AI research. Collaborate with cross-functional engineering and research teams to build modular, reusable systems, improve workflows, ensure high code quality, and implement organization-wide, low-disruption changes.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines to GPU kernels, implement low-level CUDA/cuDNN/Triton kernels, tune CPU/GPU/memory/network utilization, and develop monitoring and debugging tools for large-scale training runs.

Own and scale the pipeline that converts raw robot demonstrations (trajectory/video) into training-ready datasets. Design ontologies, run and automate annotation operations (internal and vendor), apply vision-language models for labeling and synthesis, track quality metrics, and close the loop from eval failures to targeted data collection to improve the model.

Develop, optimize, and deploy learning-based robotic manipulation and locomotion systems. Build low-latency teleoperation, train policies with reinforcement and imitation learning, diagnose control-pipeline bottlenecks, and minimize sim-to-real gaps through co-optimization of simulation and real robot behavior while collaborating with a cross-functional team.

Design, build, and maintain petabyte-scale batch and streaming data pipelines and core data infrastructure for robotics foundation model training. Own data models, storage, ingestion, transformation, and orchestration. Standardize pipelines across teleoperation and simulation datasets and collaborate with cross-functional team to support Physical AI development.

As a Full Stack Software Engineer at Doxel, you will design and develop full-stack applications, collaborating cross-functionally to optimize user experiences and facilitate data processing for construction projects.