Benevity Careers Canada: Staff AI/ML Developer in Toronto

Benevity is hiring a Staff Developer – AI/ML in Toronto, Ontario. The employer’s Ashby recruitment page lists the position as full time, hybrid and part of the Technology department.

The Ontario pay transparency range is CAD 109,400 to CAD 150,370. This is a senior technical position for an experienced software engineer who has already built and operated production machine-learning systems and can lead architecture decisions across AI infrastructure and generative AI.

A current Canada-specific Benevity internship was not verified for this search, so the CSV uses this live employer role rather than adding a student position that is no longer open or not located in Canada.

Opportunity Overview

The Staff AI/ML Developer is intended to act as a technical anchor for Benevity’s machine-learning and AI efforts. The person helps define the long-term architecture for model training, deployment, monitoring and AI-enabled product capabilities.

The job sits between deep engineering and technical leadership. It requires someone who can design systems, review architecture, mentor engineers and translate business problems into scalable technical solutions.

Available Position

• Position: Staff Developer – AI/ML
• Employer: Benevity
• Location: Toronto, Ontario
• Employment: Full Time
• Work Arrangement: Hybrid
• Department: Technology
• Ontario Pay Range: CAD 109,400-150,370

Key Responsibilities

The role designs end-to-end ML architecture from data ingestion and feature stores through model serving and monitoring. It helps establish the roadmap for AI infrastructure and orchestration and makes sure the ML ecosystem is testable, maintainable and scalable.

Benevity also expects the Staff Developer to lead architecture for LLM-powered applications such as copilots, search, assistants and agents. That can include retrieval-augmented generation, tool integrations and multi-step workflows.

Evaluation and safety are major responsibilities. The engineer creates evaluation frameworks using benchmarks, online metrics and human feedback and establishes guardrails around prompt injection, hallucination mitigation and responsible AI.

Experience Requirements

Applicants need at least seven years of software engineering experience and at least four years of experience architecting and deploying ML models in production at scale.

Benevity also expects Staff or Senior-level technical leadership, including architecture ownership and mentoring engineers. Candidates should be able to explain large technical decisions and how they influenced other teams.

Technical Skills

The role calls for deep MLOps knowledge across training, evaluation, deployment, monitoring and lifecycle management. Cloud experience in AWS, Azure or Google Cloud is important, as is data-pipeline and data-architecture knowledge.

Python and machine-learning frameworks such as TensorFlow, PyTorch or scikit-learn are relevant. The posting also highlights CI/CD, automated validation and rollback strategies, LLM system design, prompt engineering, RAG, embeddings, vector databases and agentic workflows.

The successful candidate should understand reliability, scalability, APIs, microservices, governance, privacy, bias, explainability and other responsible-AI considerations.

Education, Work Style and Compensation

Benevity lists a bachelor’s or master’s degree in Computer Science, Mathematics or a related field, or equivalent deep professional experience.

The role is hybrid in Toronto. Benevity describes flexibility around where people work and emphasizes purposeful, collaborative work. The pay transparency range is CAD 109,400-150,370.

Application Status

The employer’s Ashby job page is currently available for this Toronto position. No fixed closing date is shown, so candidates should apply while the role remains listed.

How to Apply

Apply through Benevity’s official Ashby vacancy page. The resume should demonstrate production ML architecture, MLOps, cloud engineering, AI/LLM system design and senior technical leadership rather than only model experimentation.

Tips for Applicants

Show production scale

Explain how models were deployed, monitored and operated, including data volume, traffic, latency, reliability or business impact where possible.

Separate research from engineering

Benevity needs production-grade ML. Make it clear which systems you actually shipped and maintained.

Discuss GenAI evaluation and safety

Use examples involving RAG, agents, guardrails, evaluation or monitoring and show how you handled risk as well as capability.

Final Notes

Benevity’s Staff Developer – AI/ML position is a current Toronto employer-hosted role with a published Ontario compensation range and detailed production AI requirements.