We are hiring a Senior Software Engineer to join our HCM engineering team in Toronto. You will own the design and delivery of full stack features across our HCM surfaces, working from customer problem through architecture, implementation, launch, and long-term operation. The work spans a modern React front end and cloud-native backend services, and it sits on top of business logic that has to be exactly right: calculation and eligibility rules, multi-jurisdiction compliance requirements, and integrations across the workforce stack.
This role is explicitly AI-first, in two directions. You will build AI-powered and agentic capabilities into the product, applying LLMs, retrieval, and automation to remove manual work from experiences that are still slow and form-heavy today. You will also work AI-first in how you build: using AI tooling throughout your own development loop for scaffolding, refactoring, test generation, code review, debugging, and documentation, and helping the team raise its collective bar on what good AI-assisted engineering looks like. We are not looking for someone who has read about this. We are looking for someone who already works this way and can pull others along.
As a senior engineer you are expected to operate with a high degree of autonomy. You will take ambiguous problems, decompose them, drive alignment on an approach, and deliver without needing the plan handed to you. You will also be a force multiplier: mentoring engineers, setting standards through your own code and reviews, and improving how the team works.
Responsibilities Build and deliver
- Design, build, test, and operate full stack features end to end, spanning React front end and cloud-native backend services
- Own delivery of significant pieces of the roadmap with limited direction, from technical design through production launch and iteration
- Write clean, well-tested, maintainable code and hold that standard in the code you review
- Break large, ambiguous problems into sequenced, shippable increments and communicate progress and risk clearly
- Take features all the way through: instrumentation, monitoring, rollout, experimentation, and follow-through on what the data shows
AI-first engineering
- Build AI-powered and agentic product capabilities, including LLM-backed workflows, retrieval, structured extraction, and automation of tasks that are manual today
- Establish sound patterns for working with non-deterministic systems: prompt and context design, evaluation and regression testing, guardrails, fallback behavior, cost and latency management, and observability
- Use AI tooling fluently across your own development workflow and raise the team's practice through concrete examples, shared patterns, and internal tooling
- Apply sound judgment about where AI genuinely improves an experience versus where deterministic logic is the right answer, particularly in compliance-sensitive paths
- Uphold Intuit standards for responsible AI, data privacy, and appropriate handling of sensitive employee and business data
Technical leadership and influence
- Contribute to architectural direction for your area and drive technical decisions to closure with clear reasoning and documented tradeoffs
- Raise engineering quality across the team through design review, code review, test automation, and CI/CD improvements
- Mentor engineers on the team, including onboarding, pairing, and growing their technical judgment
- Identify and drive down technical debt, reliability risk, and operational toil rather than routing them elsewhere
- Participate in on-call for services you own and improve system health based on what you learn there
Partnership
- Partner directly with Product Management, Design, Data, and QA to shape scope and turn customer problems into technical plans
- Work with adjacent HCM and platform teams on clean integration contracts and shared services
- Collaborate effectively across time zones with engineers and partners in other Intuit sites
- Stay connected to real customer usage and real customer pain, and let it inform what you build and what you push back on
QualificationsMinimum qualifications - Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
- 5 or more years of professional software engineering experience building and operating production software
- Strong full stack capability: production experience with a modern JavaScript or TypeScript front end framework, ideally React, plus a modern backend language such as Java, Kotlin, Go, Python, or Node.js
- Solid grounding in distributed systems, API design, relational and non-relational data modeling, and service-oriented architecture
- Hands-on experience with cloud infrastructure such as AWS or GCP, containerization, and CI/CD pipelines
- Demonstrated practical experience building with AI, such as LLM-backed features, retrieval-augmented systems, agentic workflows, or ML-driven product capabilities, with real production or near-production results
- Fluent use of AI development tooling in your daily engineering workflow, with specific examples of how it changed your output
- Track record of owning features end to end with minimal direction and shipping them to real customers
- Strong written and verbal communication, including the ability to write a clear technical design and explain tradeoffs to non-engineers
- Experience working in an Agile environment with a bias toward iterative delivery
Preferred qualifications - Experience in HR technology, payroll, workforce management, fintech, or another domain where correctness is auditable and financially material
- Experience designing evaluation harnesses, regression suites, or offline and online quality measurement for AI features
- Experience building rules engines, calculation or ledger-style systems, or complex configurable workflow engines
- Experience integrating third-party partner APIs or building extensible integration platforms
- Experience with front end performance, accessibility, and design system contribution at scale
- Experience mentoring engineers or acting as a technical lead on a project team
- Familiarity with data privacy and security frameworks relevant to employee data, such as SOC 2, GDPR, PIPEDA, and CCPA
- Experience modernizing or re-platforming a legacy system while keeping it running
What we look for - Ownership. You treat the problem as yours through to the outcome, not just to the merge
- Judgment. You choose the simplest approach that actually solves the problem, and you can say why
- Curiosity with rigor. You move fast on new tooling and new AI capability, and you still verify before you trust it
- Craft. You care about the quality of what you leave behind for the next engineer
- Clarity. You write and speak precisely, and you make complexity easier for the people around you
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at ). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
The expected base pay range for this position is:
Toronto $132,000 - $178,500
