Senior Staff Developer, AI and Machine Learning in Toronto

Location: Toronto, Ontario

Employment Type: Full time

Location Type: Hybrid

Department: Technology

Compensation: Ontario Pay Transparency Range CA$137,800 – CA$179,080

Meet Benevity: Benevity is the way the world does good, providing companies (and their employees) with technology to take social action on the issues they care about. Through giving, volunteering, grantmaking, employee resource groups and micro-actions, we help most of the Fortune 100 brands build better cultures and use their power for good. We’re also one of the first B Corporations in Canada, meaning we’re as committed to purpose as we are to profits.

This is a brand new role designed to shape the future of our engineering organization. As Senior Staff Developer - AI and Machine Learning, you will serve as the technical anchor for our machine learning and AI efforts, leading the end-to-end architecture of our GenAI strategy. You will bridge the gap between theoretical research and production-grade engineering—designing the scalable systems that power our model training, deployment, and monitoring. We are looking for a rare combination of deep technical mastery and strategic leadership to ensure our AI initiatives deliver high-impact, measurable business value.

What you’ll do:

• Design and oversee the development of robust end-to-end ML architecture, from data ingestion and feature stores to model serving and monitoring.
• Define the long-term roadmap strategy for our AI infrastructure and orchestration framework.
• Oversee the design, implementation, and maintenance of our AI/ML ecosystem.
• Design AI capabilities as extensible, self-service primitives (APIs, SDKs, standardized patterns) that let other domain teams build their own AI-powered features independently.
• Set the standard for MLOps - you will ensure that our ML ecosystem is as testable, maintainable, and scalable as our core application code.
• Cross-functional leadership - by working closely with Product Managers, Data Scientists and ML Engineers to translate business problems into concrete technical requirements.
• Act as a force multiplier for the team by conducting high-level design reviews and mentoring engineers on system design and performance optimization.
• Architect and evolve LLM-powered applications (e.g., copilots, search, assistants, agents), including RAG pipelines, tool integrations, and multi-step reasoning workflows.
• Design and implement robust evaluation frameworks for GenAI systems, incorporating offline benchmarks, online metrics, and human-in-the-loop feedback.
• Drive best practices for prompt engineering, agent design, and orchestration frameworks, ensuring maintainability and performance at scale.
• Establish guardrails and safety mechanisms for GenAI applications, including prompt injection defenses, hallucination mitigation, and responsible AI practices.
• Establish the golden path for model versioning, A/B testing, and automated rollbacks for identifying and mitigating drifts.
• Ensure AI architectural strategy aligns with industry best practices and standards, complies with security policies and industry regulations.
• Identify opportunities for process improvements and implement solutions to enhance platform performance and efficiency.

What you’ll bring:

• Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field, or equivalent deep professional experience.
• 8+ years of software engineering experience, with at least 4+ years architecting and deploying ML models in production at scale.
• 2+ years in a technical leadership role on building scalable platforms, technology transformation and modernization initiatives.
• Proven experience operating at a Staff or Senior level, including technical leadership, architecture ownership, and mentoring engineers.
• Experience building internal platforms/self-service infrastructure for other engineering teams to build on, not just shipping product features directly.
• Deep expertise in MLOps and production ML systems, including model training, evaluation, deployment, monitoring, and lifecycle management.
• Strong experience with cloud platforms (AWS or Google Cloud), including designing and operating scalable, distributed AI/ML workloads.
• Solid understanding of data architecture and data engineering, including data pipelines, feature engineering, data modeling, and large-scale data processing.
• Experience with ML infrastructure and tooling, such as feature stores, experiment tracking, model registries, and orchestration frameworks.
• Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn), with strong software engineering fundamentals.
• Experience with CI/CD and ML deployment pipelines, including automated testing, validation, and rollback strategies for ML systems.
• Familiarity with LLM-based systems and GenAI applications, including systems design, evaluation strategies, and observability for non-deterministic systems.
• Strong understanding of LLM architectures and trade-offs, including model selection, latency, cost, and quality optimization.
• Experience with prompt engineering and prompt orchestration, including techniques like few-shot learning, chain-of-thought, and tool/function calling.
• Experience designing and implementing RAG (Retrieval-Augmented Generation) systems, including embedding strategies, vector databases, and retrieval optimization.
• Experience building agentic workflows, including multi-step reasoning, tool use, and orchestration frameworks (e.g., LangChain, LlamaIndex, ADK, or custom frameworks).
• Strong understanding of system design for reliability and scalability, including distributed systems, APIs, and microservices architecture.
• Knowledge of data governance, model governance, and responsible AI practices (security, privacy, bias, explainability).
• Demonstrated ability to translate ambiguous business problems into scalable AI/ML solutions.
• Excellent communication and collaboration skills, with the ability to influence stakeholders and drive cross-functional alignment.

Great-to-haves:

• Certification in relevant cloud platforms or technologies.

Discover your purpose at work. We’re not employees, we’re Benevity-ites. From all locations, backgrounds and walks of life, who deserve more … Innovative work. Growth opportunities. Caring co-workers. And a chance to do work that fills us with a sense of purpose.

If the idea of working on tech that helps people do good in the world lights you up ... If you want a career where you’re valued for who you are and challenged to see who you can become … It’s time to join Benevity. We’re so excited to meet you.

Where We Work: At Benevity, we embrace a flexible hybrid approach to where we work that empowers our people in a way that supports great work, strong relationships, and personal well-being. For those located near one of our offices, while there’s no set requirement for in-office time, we do value the moments when coming together in person helps us build connection and collaboration. Whether it’s for onboarding, project work, or a chance to align and bond as a team, we trust our people to make thoughtful decisions about when showing up in person matters most.

Join a company where DEIB isn’t a buzzword: Diversity, equity, inclusion and belonging are part of Benevity’s DNA. You’ll see the impact of our massive investment in DEIB daily — from our well-supported employee resources groups to the exceptional diversity on our leadership and tech teams. We know that diverse backgrounds, experiences, skills and passions are what move our business and our people forward, so we're committed to creating a culture of belonging with equal opportunities for everyone to shine. That starts with a fair and accessible hiring process. If you want to feel seen, heard and celebrated, you belong at Benevity.

Candidates with disabilities who may require accommodations throughout the hiring or assessment process are encouraged to reach out to accommodations@benevity.com.