ShyftLabs
Sales Engineer (AI & Data Platforms)
Toronto, Ontario · Senior
Sponsorship not specified$140k-$160kDetected 118 days ago
A/B TestingSalesResearchLeadership
About the role
- We're looking for a Sales Engineer (AI & Data Platforms) to bridge the gap between enterprise data, AI capabilities, and real-world business outcomes.
- At ShyftLabs, we don't sell decks or theoretical AI.
- You will operate at the intersection of solution architecture, prototyping, and deal execution, working closely with leadership, clients, and engineering teams to move AI from experimentation to production.
Responsibilities
- Own the technical narrative in enterprise deals: Translate ambiguous client challenges into structured problem definitions and solution architectures grounded in real-world constraints such as data availability, infrastructure, and governance.
- Turn conversations into working systems (fast): Build prototypes, POCs, and demos using real or representative data to validate feasibility and demonstrate impact.
- Help clients identify where AI creates meaningful leverage versus unnecessary complexity.
- Ensure every solution is grounded in measurable outcomes and support outcome-based pricing and statement of work (SOW) development.
- Partner with leadership and delivery teams: Collaborate directly with the founding team and senior leadership on strategic opportunities.
- Drive complex enterprise deals forward: Engage both technical and business stakeholders (CIO, CDO, Product, Operations), handle objections with clarity and logic, and guide conversations toward decisive outcomes rather than prolonged exploration.
- Build prototypes, POCs, and demos using real or representative data to validate feasibility and demonstrate impact.
- We're building something big, and we want you on the journey with us.
Requirements
- This role is best suited for a candidate with a strong technical foundation in data and AI systems, a builder mindset, and the ability to connect technical decisions directly to business value.
- Nice to Have
- Salary Range
- Hybrid Flexibility: 3 days per week in our downtown Toronto office. This role is also open to candidates based out of the New York area.
- Comprehensive Benefits: 100% coverage for health, dental, and vision insurance for you and your dependents from day one.
- Real Impact: Build AI systems used by Fortune 500 companies.
- Growth & Learning: Continuous learning opportunities and influence over technical direction.
- Ownership: Shape applied research and AI strategy in a fast-growing, product-focused data company.
- Inclusion at ShyftLabs
- We're building something big, and we want you on the journey with us. If you're ready to use data and innovation to make an impact, apply today and let's grow together.
- ShyftLabs is an equal-opportunity employer committed to creating a safe, diverse, and inclusive environment. We encourage applicants of all backgrounds including ethnicity, religion, disability status, gender identity, sexual orientation, family status, age, and nationality to apply. If you require accommodation during the interview process, let us know and we'll be happy to support you.
Nice to have
- Why You'll Love Working at ShyftLabs
Compensation
- Why You'll Love Working at ShyftLabs
- Hybrid Flexibility: 3 days per week in our downtown Toronto office.
- This role is also open to candidates based out of the New York area.
- Real Impact: Build AI systems used by Fortune 500 companies.
- Ownership: Shape applied research and AI strategy in a fast-growing, product-focused data company.
- Inclusion at ShyftLabs
Benefits
- 100% coverage for health, dental, and vision insurance for you and your dependents from day one.
- We encourage applicants of all backgrounds including ethnicity, religion, disability status, gender identity, sexual orientation, family status, age, and nationality to apply.
Equal opportunity
- If you require accommodation during the interview process, let us know and we'll be happy to support you.
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This listing is sourced directly from ShyftLabs's careers page and normalized into a canonical job model.