Senior SR&ED Consultant (Software Engineer)

KPMG•Vancouver, BC

About The Position

At KPMG in Canada, our people bring their unique perspectives to Canada’s most important challenges. Here, you can build momentum that reaches beyond our business, develop skills for the future, and take ownership of your career with support at every stage. Join a firm where your career can make a difference. KPMG’s Tax Incentives Practice helps innovative organizations access federal and provincial innovation funding programs, including the Scientific Research and Experimental Development (SR&ED) program. As software, AI and technology organizations continue to invest in increasingly complex research and development, our team is seeking a Senior Consultant with a strong software engineering background, systems-level technical fluency, and hands-on experience using modern AI development tools. In this role, you will work directly with software engineers, architects, CTOs, technical leaders and KPMG tax professionals to understand complex development activities, identify technological uncertainty and experimental development, analyze supporting technical evidence, and contribute to the preparation and delivery of well-supported SR&ED claims. The role requires the ability to move beyond what a development team built and understand why the underlying technical problem was difficult, why established approaches were insufficient, what alternatives were investigated, and what was learned through the development process.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Electrical Engineering, Computer Engineering, Data Science or a related technical discipline.
Professional experience in software development, software engineering, systems architecture, technical consulting or a research and development environment.
Strong systems-level technical fluency and the ability to understand and evaluate software architectures, technology stacks and design decisions as explained by CTOs, architects and development teams.
Strong understanding of modern software engineering concepts, including software architecture, databases, cloud technologies, APIs, distributed systems, data pipelines, monitoring, testing and deployment.
Hands-on experience building software using modern development languages, frameworks and tooling.
Hands-on experience with AI coding agents and AI-enabled development tools such as Claude Code, Cursor, GitHub Copilot, Codex or comparable technologies.
Ability to rapidly understand unfamiliar technologies and technical domains.
Demonstrated ability to conduct technically grounded interviews and identify underlying engineering constraints, limitations, failure modes, experimental approaches and iterative refinements.
Strong analytical skills and the ability to distinguish routine software development from technical work involving unresolved technological challenges.
Ability to translate complex technical activities into clear and credible technical documentation.
Strong verbal and written communication skills, including the ability to communicate effectively with both technical and non-technical stakeholders.
Strong organizational skills and the ability to manage multiple client engagements and competing priorities in a deadline-driven environment.
Demonstrated initiative, ownership and sound professional judgment.
KPMG professionals are expected to approach their work with integrity, curiosity, collaboration and a commitment to quality.
Think technically: Understand systems at the architectural level and identify the engineering constraints that materially affected development.
Ask effective questions: Conduct persistent technical interviews that move beyond features and business objectives to uncover the underlying technological challenges.
Think in systems: Understand how architecture, data, infrastructure, software components and operational constraints interact.
Exercise judgment: Distinguish between routine engineering and work involving genuine technological uncertainty and experimental development.
Learn quickly: Develop sufficient understanding of unfamiliar technologies to engage credibly with experienced technical teams.
Work AI-natively: Use modern AI development tools as part of your normal technical workflow and continuously identify opportunities to improve how work is performed.
Take ownership: Identify information gaps, technical risks and next steps and proactively work with clients and engagement teams to address them.
Communicate clearly: Explain complex technical matters accurately and concisely without unnecessary technical or SR&ED terminology.

Nice To Haves

Experience with agentic technologies such as MCP, custom skills or subagents, automation agents or multi-agent workflows is considered an asset.
Knowledge of SR&ED or other innovation funding programs is considered an asset.
You are comfortable discussing architecture and engineering decisions with CTOs, architects and development teams, while continuing to ask questions until you understand the technical constraints that drove the work.
You are intellectually curious and able to move from: Feature → Implementation → Technical Obstacle → Underlying Constraint → Investigation → Technological Advancement
You are also an active user of modern AI development tools and are interested in how coding agents, automation and agentic workflows can improve the way technical work is performed.
Previous SR&ED experience is valuable, but deep SR&ED experience is not required. The successful candidate will bring the technical foundation, curiosity, judgment and learning agility required to develop strong SR&ED expertise within KPMG.

Responsibilities

Analyze complex software and technology projects to assess eligibility under the SR&ED program, with particular focus on identifying technological uncertainty, systematic investigation and technological advancement.
Conduct structured, technically grounded interviews with software engineers, architects, CTOs, product managers and other technical leaders to understand development activities and uncover experimental work that may not initially be described as research and development.
Analyze software architectures, technology stacks and engineering decisions to understand the technical constraints underlying development activities.
Investigate areas such as architectural limitations, scalability and performance constraints, distributed-system behaviour, data-model challenges, integration complexity, failure modes, unexpected results, workarounds and iterative refinements.
Evaluate why established technologies, architectures, frameworks or development approaches were insufficient under the technical constraints faced by the development team.
Understand and evaluate modern software environments involving cloud infrastructure, distributed systems, databases, APIs, data pipelines, AI and machine learning, security, identity, testing, deployment and related technologies.
Review and synthesize technical evidence from Jira, Azure DevOps, GitHub, GitLab, architecture and design documentation, testing systems, engineering records and other relevant sources.
Prepare and review high-quality technical project narratives and supporting documentation that clearly articulate technological uncertainties, hypotheses, experimental work, results and technological advancements.
Translate complex software concepts, architectural decisions and engineering trade-offs into clear, concise and defensible technical documentation.
Identify gaps, inconsistencies and weaknesses in technical positions and supporting evidence and work with clients to resolve them.
Connect technical development activities with the personnel, resource allocations and expenditures associated with SR&ED claims.
Lead technical workstreams on client engagements and coordinate deliverables with internal and external stakeholders.
Collaborate with KPMG tax professionals and multidisciplinary teams to develop comprehensive and well-supported SR&ED claims.
Support clients through CRA reviews and other SR&ED-related inquiries.
Manage multiple engagements and competing priorities while maintaining a high standard of technical quality.
Stay current with developments in software engineering, artificial intelligence, development methodologies and emerging technologies.
Successful candidates should have hands-on experience building software and solving technical problems using AI coding agents and AI-enabled development tools — beyond prompt writing or basic chatbot use.
Candidates should be comfortable using these technologies to work with code and repositories, analyze technical information, create or modify scripts, automate workflows, connect tools and data sources, and develop reusable AI-assisted processes.
Experience designing workflows where AI systems can gather context, use tools, perform work, evaluate results and iterate is particularly relevant.
Within the SR&ED practice, these capabilities may be applied to understand unfamiliar technologies and architectures; navigate and analyze large technical repositories; synthesize engineering documentation and development records; analyze Jira, Git and other engineering evidence; identify technical constraints, experiments, failures and iterative development; prepare targeted technical interview strategies; identify inconsistencies and evidence gaps across multiple sources; analyze structured and unstructured client data; develop scripts and lightweight analytical tools; and automate repeatable technical analysis and engagement workflows.
AI tools are intended to augment technical analysis and professional judgment, not replace them. Successful candidates will be expected to critically evaluate AI-generated outputs and remain accountable for the underlying technical analysis and conclusions.