AI/ML Engineer
- Toronto, ON, Canada
Full-time
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Job details
- Job type
- Full-time
- Posting language
- English
- Posted
Original job description
Who we are
For decades, insurance software has been built around rigid forms, portals and call centers that create friction for customers and drag for teams. Even as AI has advanced, most insurance work is still manual, slow and spread across disconnected systems.
General Magic builds AI agents that handle quoting, follow-ups, document collection, verification, claims intake and broker communication, connected directly to carrier, MGA and broker systems. We've raised $7.2M USD from Radical Ventures, a16z and several AI luminaries. We reward people who thrive with high autonomy, love shipping quickly and want real ownership. We work onsite in Toronto five days a week.
The role
You'll build the AI core of General Magic: the agents and LLM systems behind our product. You own how models are prompted, orchestrated and evaluated, and how they stay fast, cheap and safe in production insurance environments.
In this role, you will
Design and build agentic and multi-agent systems with tool use, state and solid failure handling
Engineer prompts and context deliberately, with guardrails and hallucination controls
Cut cost and latency with concrete levers like caching, context structuring and model selection
Build eval pipelines that catch regressions and tie quality to every release
Work across our backend, APIs and data models to ship features end to end
For this role, you should have
A deep, first-principles understanding of how LLMs work, not just API usage
Real agentic systems running in production, not demos
Strong backend fundamentals and the ability to move through unfamiliar codebases quickly
Experience treating evals and safety as core engineering
The drive to ship fast in a high-velocity setting
$120-200k, equity, new MacBook Pro and more.
More about this job
Responsibilities
You will design and build agentic and multi-agent systems, including prompt engineering and the development of evaluation pipelines. You will also work across backend systems and APIs to ensure features are delivered end-to-end with a focus on performance and safety.
Requirements
Candidates must have a deep, first-principles understanding of LLMs and proven experience deploying agentic systems in production environments. Strong backend fundamentals and the ability to work effectively in a high-velocity, autonomous setting are required.
Skills
- AI
- Machine Learning
- LLM
- Prompt Engineering
- Agentic Systems
- Backend Development
- API Development
- Data Modeling
- Evaluation Pipelines
- Latency Optimization
- Cost Optimization
- Safety Guardrails
- Hallucination Control
- System Orchestration
Benefits
- Equity
- New MacBook Pro
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Engineering
- Data & Analytics
Keywords
- AI
- Machine Learning
- LLM
- Agentic Systems
- Multi-agent Systems
- Prompt Engineering
- Backend
- API
- Data Models
- Latency
- Caching
- Evaluation Pipelines
- Insurance Software
- Production Environments
- Software Engineering
- Guardrails
- Hallucination Controls