The Principal Software Engineer is a recognized technical expert with deep domain knowledge, responsible for designing and implementing software architecture for current and future products. This role evaluates existing and new designs for quality, scalability, and long-term viability.
Locations (2)
Canada
United States
Compensation
The base salary range for this role is C$180,000 - C$243,000. Note that salary may vary based on location, skills, and experience and may vary from the amounts listed above. This position may also be eligible for a variable bonus in addition to base salary as well as health coverage, paid holidays, and other benefits.
Responsibilities
Drive technical strategy and architecture within one or more product lines
Own end-to-end architecture for the product line
Translate architectural direction into actionable designs and implementations
Lead complex, cross-functional, high-impact technical initiatives
Ensure consistency and quality of design across teams
Requirements
Bachelor’s degree in information systems, computer science, engineering, related discipline, or equivalent work experience
Forward Deployed Engineer (FDE) - GenAI / Agentic AI
A Forward Deployed Engineer who thrives at the intersection of client problem-solving and hands-on AI engineering, partnering directly with clients and engineering teams to turn complex business challenges into production-grade GenAI and Agentic AI solutions.
Locations (2)
Canada
United States
Responsibilities
Embed with client engineering and business stakeholders to design, build, and deploy end-to-end production solutions on Databricks within accelerated timelines.
Architect scalable data pipelines using Delta Lake, Unity Catalog, and Apache Spark, handling complex cloud migrations, legacy BI modernization, and performance/cost optimization.
Build and deploy enterprise AI/ML workflows, knowledge layers/ontologies, and natural-language analytics solutions using Databricks AI/BI Genie, Mosaic AI, RAG architectures, and Model Serving.
Lead architectural workshops, translate business requirements into technical roadmaps, and troubleshoot production bottlenecks alongside client teams.
Act as the primary technical bridge between Tiger Analytics delivery teams, client stakeholders, and Databricks field engineering teams to drive adoption and expand platform capabilities.
T
Member of Technical Staff, Reinforcement Learning
True Talent Solutions Partners · Posted Nov 13, 2026
Design and build reinforcement learning environments for complex clinical reasoning and evaluate them to improve clinical decision-support tools.
Compensation
Base salary: $250,000 to $400,000 USD annually.
Responsibilities
Design and build reinforcement learning environments for complex clinical reasoning, including multi-step tasks, tool use, state transitions, and measurable outcomes.
Create and calibrate reward functions, grading rubrics, and LLM judges, and test them for reward hacking and misleading scores.
Own the workflow from task design and data creation through implementation, evaluation, and delivery to model-training teams.
Run post-training and agent-evaluation experiments, then use observed failures to improve the environments.
Collaborate with clinicians and technical leadership to validate model outputs and guide the work.
Requirements
PhD in computer science, machine learning, or a related field
Bachelor's degree or higher in a relevant technical field
Provide cybersecurity expertise throughout the AI and software development lifecycle, ensuring secure design, development, deployment, and operation of AI and cloud applications.
Responsibilities
Work closely with AI developers, software engineering teams, cloud engineering, enterprise architecture, data teams, and business stakeholders to embed security into pro-code development
Act as the SME for Microsoft Azure cloud security, application security, identity and access management, AI security, and the NIST Cybersecurity Framework
Provide cybersecurity expertise and technical guidance for enterprise AI, generative AI, LLM, agentic AI, and conversational AI solutions
Perform security assessments across the complete AI application lifecycle
Assess the security of LLM-based applications, AI agents, Chat UIs, APIs, plugins/tools, data sources, retrieval systems, and application integrations
Soutenir les équipes d'investissement en participant à l'analyse de la performance des portefeuilles, au suivi des investissements, ainsi qu’à l'élaboration de méthodes quantitatives pour le suivi et le développement du portefeuille.
Compensation
Rémunération globale concurrentielle avec régime de retraite à prestations déterminées et programme de coïnvestissement
Responsibilities
Analyser les données de transactions, de positions et de performance des portefeuilles, et produire des analyses claires pour les équipes d’investissement
Concevoir et maintenir des modèles analytiques et des tableaux de bord (Power BI, SQL, Python) qui soutiennent la prise de décision
Contribuer à la qualité des données et au développement des pipelines de données qui les alimentent
Traduire les besoins d’affaires des équipes de Revenu fixe en solutions analytiques claires et documentées
Explorer l’apport des méthodes d’apprentissage machine et de l’IA générative à l’analyse des données de marché
Requirements
Diplôme universitaire de deuxième cycle en finance, mathématiques, actuariat, ingénierie financière, informatique ou discipline équivalente
Soutenir les équipes d'investissement en participant à l'analyse de la performance des portefeuilles, au suivi des investissements, ainsi qu’à l'élaboration de méthodes quantitatives pour le suivi et le développement du portefeuille.
Compensation
Rémunération globale concurrentielle avec régime de retraite à prestations déterminées et programme de coïnvestissement
Responsibilities
Participer au développement des applications, des modèles, des outils ou des perspectives pour des applications pratiques de support à la prise de décision en investissement à l’aide de méthodes quantitatives
Intégrer des capacités d’IA générative dans l’analyse de données de crédit privé
Collaborer à la conception et au maintien de pipelines d’ingestion et transformation de jeux de données structurés et non-structurés
Collaborer avec les équipes d’investissement et autres secteurs d’affaires pour intégrer les solutions analytiques dans les processus décisionnels
Contribuer aux projets transversaux ainsi qu’à l’évolution des meilleures pratiques et façons de faire en science de données au sein de l’écosystème analytique
Requirements
Diplôme de deuxième cycle dans un domaine quantitatif comme l'informatique, les mathématiques, la science des données ou l'ingénierie financière
Develop, enhance, and implement methodologies and models for counterparty credit risk measurement, derivatives exposure analytics, trading limit monitoring, and regulatory capital.
Responsibilities
Design, develop, enhance, and implement methodologies and models for counterparty credit risk measurement, derivatives exposure analytics, trading limit monitoring, and regulatory capital.
Manage the full model and methodology lifecycle, including development, testing, implementation, documentation, performance monitoring, recalibration, remediation, and ongoing review.
Work in cross-functional workstreams to enhance methodologies, improve analytical infrastructure, and strengthen risk measurement capabilities.
Build strong relationships with stakeholders across Capital Markets, Risk Management, Technology, Finance, Model Validation, Audit, and Compliance.
Translate complex quantitative concepts, methodology decisions, and model-related risks into clear, concise, and actionable insights for a range of audiences.
Requirements
Master's or PhD in a quantitative field including mathematics, statistics, financial engineering, economics, physics, or a related discipline
As a Sr. Consultant, Data Developer, you’ll be a senior technical leader in CAM’s technology transformation, modernizing the collections, recovery and insolvency platforms that support CIBC Retail, Small Business and Simplii clients. You’ll own the end-to-end data solution for CAM, setting the technical direction and leading the design, development, testing and implementation of the CAM data layer, including ETL pipelines, an operational data store (ODS) and large-scale data migration.
Responsibilities
Define and own the target-state data architecture, design patterns and engineering standards for the CAM data layer.
Lead and contribute to the development of complex, high-volume data engineering solutions, including metadata-driven ETL pipelines.
Act as the accountable technical owner for CAM data applications, driving the roadmap, delivery planning, estimates, production stability and continuous improvement.
Lead the technical design and integration of cross-functional, multi-platform systems, including batch file and API integrations.
Own the data migration strategy and approach, including mapping, profiling, transformation, reconciliation, mock conversions, cutover and post-implementation verification.
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