Top Python Jobs in Toronto

Lead pre-silicon emulation strategy for a next-generation AI inference ASIC using Cadence Palladium. Architect multi-rack models, enable software left-shift via hybrid emulation, verify high-speed interfaces and memory hierarchies, perform power/performance analysis, and collaborate with RTL, DV, software teams and Cadence R&D to ensure silicon readiness.

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.

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.

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.

The Marketing Operations Specialist will implement and maintain marketing technology, manage data flows, configure tracking, and optimize performance across marketing efforts.

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.

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.

The Lead Software Engineer will design and develop full-stack applications for construction project management, ensuring intuitive data workflows and collaborating across teams to enhance user experience and performance.

As a Senior Agentic AI Engineer, you will lead innovation in AI initiatives, from ideation to prototyping, collaborating with teams to deliver impactful solutions.

Lead liquidity forecasting, scenario analysis and credit facility management for Northleaf funds. Recommend financing strategies, optimize borrowing capacity, manage third-party providers, build reporting and automation, and mentor junior team members to support efficient capital deployment across private equity, private credit and infrastructure funds.

Lead development of back-end systems and end-to-end data/ML pipelines, build and deploy generative AI workflows, design and consume performant APIs, contribute to AI/ML architecture, mentor engineers, and communicate progress to leadership.

Develop and maintain backend services and data pipelines to support AI/ML and GenAI workflows. Build and consume secure, scalable APIs, work with microservices on Kubernetes, collaborate on design and code review, and grow applied ML skills under senior mentorship.

Drive strategy and execution for simulation and neural reconstruction products: conduct customer discovery, define roadmaps, translate technical AI and simulation needs into scalable features, track success metrics, collaborate with engineering and GTM, and support launches. Role includes frequent customer interaction and on-site visits.

Build, train, evaluate, and deploy production computer vision models (detection, segmentation, OCR, barcode decoding, visual search). Own dataset quality, tooling, model releases, inference exports, and monitoring. Collaborate cross-functionally to improve model performance, robustness, and deployment for retail robotics and edge environments.

Lead design, integration, and optimization of production robot perception pipelines (detection, segmentation, depth, tracking, SLAM). Bridge computer vision and robot autonomy, optimize real-time inference on edge GPUs, debug field failures, define system benchmarks, and provide technical leadership and mentorship across teams.