Top Python Jobs in Toronto

Hands-on photonics engineer to design, run, and automate phase and polarization experiments, characterize hardware, refine physical and numerical models, and iterate device designs to reduce optical loss and improve system performance while managing thermal and vibration mitigation.

The Senior Backend Engineer will architect solutions, deploy features, maintain APIs, design service communication, and collaborate with teams to ensure product quality and scalability.

As a Senior Platform Engineer, you will design an Internal Developer Platform, maintain Azure/AKS environments, build AI-driven automation, and mentor junior engineers while ensuring system reliability and scalability.

Lead analytics consulting engagements: translate business problems into end-to-end data science and ML solutions, present to executive stakeholders, manage projects and global teams, and operationalize analytic outputs with IT and data engineering.

Design, implement, and manage Azure cloud solutions with a focus on automation. Develop CI/CD pipelines, ensure security compliance, and mentor IT team members.

Design and productionize ML systems for information extraction, retrieval, ranking and recommendation. Own full ML lifecycle from feature engineering to deployment, monitoring, and evaluation. Collaborate with data, infra, and product teams to build scalable, production-grade ML infrastructure and observability for both streaming and batch inference.

Design, build, and deploy scalable AI/ML solutions: end-to-end pipelines, fine-tune LLMs, production model deployment with CI/CD, serve models via microservices/APIs, monitor model performance, collaborate with data/cloud teams, and prototype/evaluate new AI frameworks using Python, SQL, Spark and MLOps tools.

Use customer, digital, and transportation data to build targeting, propensity, pricing, loyalty, and churn models; run experiments (A/B), optimize promotions and pricing, present insights to stakeholders, manage data structures in Databricks/AWS, and lead project work with business partners to drive data-driven decisions.

Design, build, and operate CI/CD, Kubernetes-based platforms, deployment automation, and observability for engineering workflows. Improve reliability, performance, and scalability across cloud and on-prem environments, debug cross-boundary failures, perform root-cause analysis, and deliver durable platform software and self-service tooling.

Operate and scale production AI inference infrastructure, run releases and capacity changes, build self-service CD pipelines and automation, extend telemetry and observability, collaborate on SLOs, post-mortems, and capacity planning to reduce operational toil.

Design, implement, and maintain Python frameworks and services that orchestrate distributed engineering workflows across machines and clusters. Build scheduling, execution, resource management, failure recovery, and test infrastructure. Define APIs and abstractions, reason about concurrency and distributed-systems behavior, debug complex multi-system issues, write automated tests and documentation, and partner with platform, CI, release, QA, and product teams to deliver scalable infrastructure.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly collection/analysis, visualization and root-cause tools, failure classification and monitoring frameworks. Extend compilers, runtimes and programming interfaces for profiling and instrumentation, improve bring-up and low-level debug workflows, lead cross-functional initiatives, support incident response, and establish debuggability and reliability best practices.

Design, implement, optimize, and validate high-performance ML and linear algebra kernels for Cerebras hardware. Develop low-level assembly and CSL routines, use mathematical performance models, create unit/system tests, and collaborate with chip and system architects to maximize compute utilization and scale kernels for state-of-the-art AI/HPC workloads.

Design and implement high-performance distributed runtime components for large-scale training and inference. Optimize data and communication pipelines, enable scalable multi-node execution, collaborate with ML and compiler teams, diagnose performance issues via profiling, and contribute to system architecture and roadmap for cutting-edge AI workloads.

Integrate, validate, and productionize cross-stack inference features across AI frameworks, runtime, compiler, kernels, distributed systems, and hardware. Drive zero-to-one projects, debug system-wide failures, manage accelerated timelines, and improve automation, diagnostics, and repeatable integration practices while collaborating across software and hardware teams.

Implement, optimize, and validate high-performance machine learning and linear algebra kernels for the Cerebras Wafer-Scale Engine. Develop low-level kernel routines using the Cerebras Software Language and C++, apply parallel programming techniques, profile and tune performance, debug correctness and hardware utilization issues, build tests and validation, and collaborate with compiler, performance, and hardware engineers.

Early-career SDET on Release Integration Testing for Cerebras AI Inference Core. Write automation and tests across the AI stack, triage cross-stack failures, maintain branch stability, collect qualification evidence, and collaborate with feature and infra teams to prepare features for production release.

Implement and scale LLM training, fine-tuning, and post-training techniques (RL-based). Build evaluation and data pipelines, debug ML stack issues, optimize training/inference workflows, and ship maintainable ML infrastructure code.

Design and develop software to automate bare-metal configuration, orchestration, scheduling, and job placement for large wafer-scale clusters. Build upgrade/patch workflows, monitoring, HA failure handling, visualization and alerting, and user/admin tools. Ensure on-premise and cloud deployment support and develop tests and debugging tools for distributed systems.

Drive end-to-end ML model inference performance: build kernel- and system-level performance models, optimize kernel microcode and compiler algorithms, debug runtime performance on system and cluster, and develop tooling to visualize and analyze performance data from the Wafer Scale Engine and compute cluster.

Build, productionize, and optimize a GPU-based inference stack combining GPU prefill with Cerebras decode. Implement and operate model-serving APIs, vLLM/PyTorch/ROCm runtimes, deployment and reliability practices, performance profiling and optimization, cross-layer debugging, numerical validation, and benchmarking/infrastructure for production inference at scale.

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.