Top Research Jobs in Toronto

The role involves researching and prototyping security technologies for OS and device protection, analyzing threats, and developing innovative security features.

The Database Research Expert will lead the architecture design for a data analytics platform, optimize database performance, and guide research on innovative database technologies.

Lead the development of competitive bus architectures and interconnection protocols for AI and computing. Innovate in chip design and ensure business success through system-level solutions.

The Research Engineer will design agentic software systems, focusing on AI agents' collaboration in software engineering, prototype frameworks, and optimize engineering processes.

Develop and implement quantitative trading models, analyze financial data, and create new trading indicators. Strong analytical skills required.

As a Research Machine Learning Scientist, you will develop and apply deep learning techniques on large-scale datasets while collaborating with engineers. You will also research and innovate in AI applications to improve banking services.

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.

Manage full-cycle recruiting, including sourcing, screening, interviews, candidate communication, offers, background checks, and onboarding transitions. Partner with hiring managers to define recruitment needs and strategies, maintain applicant tracking systems, support employer branding and social media initiatives, produce recruiting analytics, and lead talent acquisition projects. The role requires bilingual French and English communication, HR knowledge, confidentiality, and familiarity with clinical practices and Health Canada and FDA guidelines.

Build and own ML infrastructure for distributed training, inference, experiment tracking, model registries, and CI/CD. Enable active-learning and closed-loop optimization, collaborate with computational scientists, and mentor colleagues to translate research prototypes to production-grade systems.

Leads portfolios of dermatology and rheumatology clinical studies, overseeing Project Managers, budgets, vendors, timelines, quality, regulatory compliance, risk mitigation, client relationships, and proposal development. The role manages multinational Phase III trials and provides therapeutic and operational leadership across study teams.

Act as a technical advisor to academic AI labs working on foundational models. Identify research workloads suited to NVIDIA platforms, engage researchers to remove infrastructure blockers, track frontier research, partner with internal teams to drive adoption, translate academic feedback into product and program improvements, and support NVIDIA participation at major research venues through content, workshops, and lab programs.

This role involves researching MEA types, developing novel MEAs, preparing reports, collaborating with teams, and writing scientific publications.

Manages clinical trial investigator sites from activation through close-out, ensuring patient safety, GCP compliance, protocol adherence, data quality, and timely study execution. Responsibilities include onsite and remote monitoring, site relationship management, investigator and staff training, recruitment support, adverse event follow-up, investigational product oversight, issue resolution, CAPA remediation, documentation, audit support, and database release activities. Requires extensive clinical research monitoring experience and frequent travel.

Conducts remote and onsite clinical trial monitoring visits, including site qualification, initiation, interim, and close-out activities. Ensures protocol, ICH GCP, regulatory, and SOP compliance; verifies source data, manages investigational product accountability, reviews regulatory documentation, and supports adverse event reconciliation. The role addresses site issues, recruitment, training, audits, budgets, CTMS activities, and sponsor communication while mentoring CRAs and coordinating with cross-functional clinical teams.

Conducts remote and onsite clinical trial monitoring visits, ensuring protocol, ICH GCP, FDA, regulatory, and sponsor SOP compliance. Reviews source documents, CRF data, regulatory binders, and trial files; manages investigational product accountability, adverse event reconciliation, site issues, recruitment, and audit preparation. Supports monitoring plans, budgets, training, CTMS activities, sponsor communication, and CRA mentoring while coordinating with cross-functional clinical teams.

Conducts remote and onsite clinical trial monitoring visits, including qualification, initiation, interim, and close-out visits. Ensures protocol, ICH GCP, FDA, regulatory, and SOP compliance; verifies source data and investigational product accountability; reviews regulatory documents; manages site issues, adverse event reconciliation, recruitment, and audit preparation. Supports monitoring plans, budgets, training, CTMS maintenance, CRA mentoring, sponsor communication, and cross-functional issue resolution.

Conducts remote and onsite clinical trial monitoring visits, including site qualification, initiation, interim, and close-out visits. Ensures protocol, ICH GCP, FDA, regulatory, ethical, and SOP compliance; verifies source data, manages investigational product accountability, reviews regulatory documents, supports recruitment, resolves site issues and data queries, and prepares monitoring reports. The role also supports monitoring plans, audits, budgets, CTMS activities, CRA training, mentoring, and sponsor-site communication.

Lead research and design of large-scale Biological Foundation Models (BioFMs) using genomic and single-cell data. Develop novel architectures and pretraining paradigms, implement and train models at scale, collaborate with biologists and drug developers, mentor junior researchers, and publish findings in top-tier venues.

The Lead Research Engineer will provide technical leadership, develop scalable ML solutions, and collaborate with teams to innovate and deliver products that enhance customer experiences in legal, accounting, and government sectors.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

As a Research Engineer in Sensor Simulation, you will develop multi-sensor rendering systems for self-driving technologies, collaborate with scientists and engineers, and work on innovative solutions using machine learning and neural rendering techniques.

The role involves designing and implementing machine learning systems for knowledge representation and temporal reasoning, focusing on organizational intelligence. Responsibilities include building LLM-powered pipelines, developing memory algorithms, and researching various ML techniques.

Build and scale core research infrastructure and agentic systems across backend, frontend, and model integrations. Own reliability, observability, and performance; ship research-proven features to production rapidly and develop shared tools and SDKs to accelerate Research, QA, and Engineering.

Directs clinical research billing operations, including coverage analysis, study calendars, charge review, budgeting, sponsor invoicing, compliance audits, revenue-cycle activities, and financial reporting. Leads staff, develops policies and training, manages Epic and OnCore workflows, partners with research, finance, compliance, audit, and clinical teams, and drives process improvements, regulatory readiness, billing accuracy, and financial sustainability.

Build post-training infrastructure for voice and text agents, including training environments, verifiers, synthetic data pipelines, evaluations, and end-to-end experiments. Select and adapt open-weight models, design robust rewards, deploy models to production, and improve behavior using real-world usage. The role requires strong Python engineering, experience taking models from raw data to production, GPU familiarity, and collaborative remote work.