General Information:Job Title: AI Engineer
Location: Toronto, ON (Onsite/Hybrid)
Job Type: Full-Time
Hiring Timeline: Immediate
Reporting Line: Head of R&D
Existing Vacancy: Yes
Salary Range: 135K - 170K CAD per year
(negotiable)Role Overview:This is a
high-impact, senior engineering role, where engineers are expected to operate with significant ownership and minimal oversight. The role focuses on building production-ready AI systems in an environment where speed, correctness, and architectural decisions have long-term implications.
Ideal Candidate's Profile:A seasoned AI engineer (
ninja-level) with hands-on experience in developing and deploying real LLM systems, who excels in environments with significant ownership responsibilities and values impactful work more than structured, low-risk settings.
Individuals driven by
ownership, autonomy, and the opportunity to build from the ground up (rather than being a small cog in a large organization) will thrive here.
Responsibilities & Expectations:Key Responsibilities:- Design and build production-grade LLM systems (RAG, agents, APIs)
- Architect systems that minimize rework in fast-evolving environments
- Own end-to-end delivery of critical AI features
- Define and implement evaluation frameworks
- Optimize systems for cost, latency, and reliability
- Collaborate across teams where needed
- Provide technical guidance where applicable (especially for less experienced engineers on adjacent teams)
Must-Haves (non-negotiables):- Strong backend/software engineering foundation (Python, APIs, system design)
- Proven experience shipping LLM-powered features to production (non-negotiable)
- Deep expertise in:
- RAG systems (advanced retrieval + evaluation)
- LLM evaluation methodologies (golden sets, regression testing)
- Prompt engineering at API level
- Agent architectures (ReAct, tool calling, planning loops)
- Strong understanding of trade-offs (cost, latency, scalability)
- Ability to work independently in ambiguous, fast-moving environments
Nice-to-Have:- Fine-tuning experience (LoRA, SFT, DPO)
- Inference stack experience (vLLM, TGI, llama.cpp)
- Observability tooling (Langfuse, LangSmith)
- Prior experience in early-stage or high-ownership teams
- Public work (GitHub, blogs, talks) demonstrating depth
Education:- Bachelor's or master's degree in computer science or a related discipline
Technical Skills:- Advanced Python and backend engineering
- LLM systems (RAG, agents, prompting, evaluation)
- API design and system architecture
- Docker, Git, CI/CD
- Understanding of inference systems and scaling
Soft Skills:- High ownership and accountability
- Ability to operate in ambiguity ("build while flying")
- Strong decision-making and trade-off analysis
- Clear communication with cross-functional teams
What Success Looks Like in the First 90 Days:By the end of Month 1:- Deeply understand Crosstalk/Zync architecture and ongoing projects
- Contribute meaningfully to ongoing systems (not just onboarding tasks)
- Identify gaps or risks in current implementations
By the end of Month 2:- Own and deliver a critical feature or system component end-to-end
- Improve an existing system (performance, evals, or architecture)
- Demonstrate strong independent execution
By the end of Month 3:- Act as a trusted senior engineer on the team
- Drive architectural decisions or improvements
- Deliver measurable impact (system reliability, quality, or efficiency)
- Operate with minimal oversight in high-stakes projects
Perks you'll appreciate:- Employee Health: Comprehensive health and dental coverage for you and your family
- Time Off: Competitive paid time off and flexible leave policies
- Retirement: Retirement savings programs and employer contributions
- Professional Growth: Dedicated learning and development budget
- Flexible Work: Remote and hybrid work options
- Perks: Equipment allowances, internet reimbursements, business travel coverage, and employee stock options (ESOP), where applicable.
- Community Engagement: Team events, meetups, and company offsites
