Benevity is hiring a Senior Staff Developer AI and ML in Toronto!
Benevity, a Canadian B Corporation that builds giving, volunteering and grantmaking software for many of the Fortune 100, is hiring a Senior Staff Developer for AI and Machine Learning in Toronto. This is a newly created position intended to act as the technical anchor for the company's generative AI and ML ambitions, owning the end-to-end architecture behind model training, deployment and monitoring. The role sits between research and production engineering: the successful candidate will treat AI capabilities as reusable, self-service building blocks — APIs, SDKs and standard patterns — so that other product teams can ship their own AI features, while holding the ML platform to the same standards of testability and maintainability as the core application. It also covers LLM application architecture, evaluation and safety, and long-range infrastructure roadmap planning. Benevity describes its working style as flexible hybrid, with no fixed in-office quota but an expectation of coming together in person for onboarding, projects and team connection.
Key responsibilities
- Own the end-to-end ML architecture, spanning data ingestion, feature stores, model serving and monitoring.
- Set the long-term roadmap for AI infrastructure and the orchestration framework.
- Package AI capabilities as extensible self-service primitives such as APIs, SDKs and shared patterns for domain teams.
- Establish MLOps standards covering testing, maintainability, model versioning, A/B testing and automated rollback for drift.
- Architect LLM-powered products including copilots, search, assistants and agents, with RAG pipelines, tool integrations and multi-step reasoning.
- Build evaluation frameworks for GenAI combining offline benchmarks, online metrics and human-in-the-loop feedback.
- Define guardrails and safety controls such as prompt injection defenses, hallucination mitigation and responsible AI practices.
- Partner with product managers, data scientists and ML engineers to turn business problems into technical requirements, and mentor engineers through design reviews.
Key requirements
- Bachelor's or Master's degree in computer science, mathematics or a related field, or equivalent professional depth.
- 8+ years of software engineering experience, including 4+ years architecting and running ML models in production at scale.
- 2+ years of technical leadership on scalable platform builds, transformation or modernization work, at Staff or Senior level.
- Experience creating internal platforms and self-service infrastructure that other engineering teams build on.
- Deep MLOps knowledge across training, evaluation, deployment, monitoring and lifecycle management.
- Hands-on cloud experience with AWS or Google Cloud, including distributed AI/ML workloads and data architecture.
- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch or scikit-learn, plus strong engineering fundamentals and CI/CD pipelines.
- Familiarity with LLM systems: model selection trade-offs, prompt engineering, RAG and vector databases, and agentic frameworks like LangChain or LlamaIndex.
- Knowledge of data and model governance, security, privacy, bias and explainability; cloud certification is an asset.
- Seniority: Senior Staff
- Work mode: Hybrid
- Key skills: MLOps, Python, TensorFlow, PyTorch, scikit-learn, AWS, Google Cloud, RAG, LLM orchestration, Prompt engineering, Feature stores, CI/CD, Distributed systems, Responsible AI, Agentic workflows
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