Top Python Jobs in Toronto

Lead design, development, testing, implementation and support of data and AI products. Translate business requirements, provide technical direction, mentor developers, build automation and frameworks, improve delivery processes, and perform data analysis, profiling and regression testing.

Own and ship production ML models end-to-end: problem framing, model design, training pipelines, deployment to served endpoints, and model-quality monitoring. Design experiments, detect drift, run champion-challenger evaluations, and partner with Data Engineering, backend, product and QA to integrate model outputs that drive business outcomes in fintech.

Lead R&D on LLM training and inference optimization, train and evaluate large-scale models on Tenstorrent hardware, improve performance via techniques like speculative decoding, quantization, kernel fusion, flash attention and distributed training, diagnose system bottlenecks, and translate research into scalable production solutions through cross-functional collaboration.

Seeking a Senior Software Development Engineer in Test to architect and maintain test infrastructure, automation frameworks, and build CI/CD pipelines for cloud-based products. Responsibilities include mentoring, improving test strategies, and ensuring software quality and performance.

Build and scale backend systems for Knak’s AI-powered marketing automation platform. Develop Laravel APIs, Python and Node.js AI microservices, and scalable AWS infrastructure. Optimize MySQL databases, support observability and performance, maintain code quality through testing and reviews, collaborate cross-functionally, contribute to architecture decisions, and mentor engineers.

Lead the frontend digital design of D2D PHY for chiplet systems, optimizing performance and collaborating across teams while mentoring junior designers.

The Senior Product Manager will manage data quality features for the Compass Platform, collaborating with teams to enhance data integrity and deliver product features. Responsibilities include designing specifications, managing backlogs, and ensuring product releases align with customer needs.

Build and own robot autonomy systems spanning task, behavior, path, and trajectory planning. Design long-horizon behavior architectures with recovery and operator handoff, coordinate multiple robots, integrate LLM/VLM reasoning safely, and define interfaces between learned policies and classical planners. Evaluate performance through simulation, log replay, regression testing, and real-robot field operation while collaborating across perception, controls, simulation, and platform teams.

Develop automated tests and genomic data analysis pipelines to validate code deployments, detect errors, and ensure the accuracy and integrity of clinical-grade sequencing workflows. Collaborate with software engineers and variant curators to improve bioinformatics processes and prevent production issues. The role combines bioinformatics expertise, large-scale genomic data analysis, software quality engineering, and test automation in a fully remote genomic health platform.

Design, build, operate, and maintain critical distributed systems powering Grafana Cloud Metrics and the open-source Mimir project. Drive projects from concept through production, improve reliability and performance, contribute to roadmap and design decisions, review code, support teammates, manage releases, and participate in follow-the-sun on-call rotations. Work with cloud infrastructure, microservices, observability systems, and large-scale telemetry databases while collaborating in a remote, open-source environment.

Coordinate human-subject recording sessions, including participant preparation, sensor setup, calibration, stimulus delivery, signal monitoring, and documentation. Perform daily system quality control, manage recruitment and scheduling, maintain consent and participant records, develop SOPs, and conduct first-pass data operations using Python. Collaborate with program leadership to refine acquisition protocols and improve system performance.

Provides technical and functional leadership for epidemiological and real-world evidence statistical programming. Leads hands-on development and validation of SAS and R programs, analysis datasets, tables, figures, and reproducible workflows using healthcare data. Mentors programmers, oversees internal and CRO deliverables, establishes standards and quality processes, supports regulatory and scientific outputs, and collaborates with epidemiologists, statisticians, and data scientists across projects and therapeutic areas.

Lead design and implementation of a production-grade LLM inference platform: deploy and serve models on GPU infrastructure, optimize latency and cost with quantization/batching/caching/routing, build infrastructure in Python or Golang, operate serving frameworks (vLLM, SGLang, TensorRT-LLM), and own the inference roadmap working with CTO and Product.

Lead and grow an engineering team building SELECT, a cloud cost management platform. Drive technical strategy, architecture, development practices, reliability, and cross-functional execution. Mentor engineers, manage performance and hiring, and improve delivery processes to scale the product and organization.

Develop and maintain AV automation tools and distributed test platforms to measure device audio/video quality. Build front-end and backend services, operationalize AV metric pipelines, and collaborate with internal teams and external vendors to validate device capabilities and roll out AV-related applications.

Own end-to-end product analytics for the Pro team: build production-grade pipelines and dbt models, define north-star metrics, design A/B tests and causal inference frameworks, create dashboards and metric frameworks, embed AI tooling, and translate insights into product recommendations for leadership.

Lead real-time incident management across online and physical operations, coordinate cross-functional response, drive diagnosis, recovery, RCA documentation, and continuous improvement of SRE, release, and incident processes.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Lead hands-on R and SQL programming for real-world evidence studies using claims and EHR sources (Flatiron, Optum). Clean/validate RWD in Snowflake, implement RWE protocols, apply epidemiologic and statistical methods, create documentation, dashboards, and reporting, and collaborate with study teams to meet timelines.

Embed with client engineering teams to deploy, integrate, and scale enterprise generative AI solutions. Build agentic systems, train and serve LLMs with Vertex AI, integrate models with BigQuery/AlloyDB/Spanner, implement streaming inference pipelines, and ensure operational reliability while collaborating with stakeholders to translate business goals into production-grade architectures.

Design, build, and maintain batch and real-time data pipelines and ETL/ELT processes using Oracle and cloud data platforms. Optimize SQL and stored procedures, ensure data quality and compliance, automate data workflows, analyze data, and mentor junior engineers.

Serve as a technical, customer-facing support engineer who diagnoses root causes, configures and improves AI-driven workflows, builds lightweight automations and scripts (SQL/Python/JS), synthesizes patterns for Product and Engineering, mentors teammates, and drives measurable reductions in case volume and improvements in customer experience.

Lead complex people-analytics work: design actionable dashboards, apply rigorous research methods, translate analysis for executives, advise HR leaders, build AI-driven workflows, and work hands-on with Visier, Snowflake, SQL, and Python/R.

Lead design and delivery of scalable backend systems powering 3D and computer-vision experiences. Build mission-critical APIs, ETL/data pipelines, and distributed services; mentor senior engineers; drive architecture, observability, resiliency, and cross-team initiatives to ensure performance at global scale.

Build and maintain internal AI enablement tools, workflows, and guardrails to help engineers use coding agents and LLMs safely. Evaluate models, partner with teams to apply AI, track adoption and impact, and provide documentation, coaching, and advocacy to increase engineering velocity.