Manager, AI Systems Engineering
Toronto, Ontario, Canada
Benevity
Benevity's corporate purpose software offers the only integrated suite of community investment, employee, customer and nonprofit engagement solutions.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. We have people working all over the world, including Canada, Spain, Switzerland, the United Kingdom, the United States and more!
We’re seeking an experienced and technically skilled Manager of ML/AI Systems Engineering to lead the development, scaling, and operation of ML/AI infrastructure and services within our B2B SaaS platform. In this role, you’ll guide the team in delivering secure, scalable, and high-performing ML-powered features by building on industry-leading PaaS tools and ensuring reliable system operations.
You’ll lead engineering execution in a Scrum environment, partnering closely with product engineering, platform, and DevOps teams to deliver production-ready, well-integrated, and observable ML/AI services.
What you’ll do:
Team Leadership & Delivery
- Lead, mentor, and support a collaborative team of ML/AI systems engineers and operations specialists
- Guide Agile delivery practices, including sprint planning, retrospectives, and backlog prioritization in alignment with product and platform goals
- Manage the ML/AI systems engineering backlog to ensure focused and impactful execution
- Ensure high availability, performance, and reliability of ML/AI services in production environments
ML/AI Platform Engineering & Operations
- Build and enhance infrastructure for training, deploying, and monitoring ML models on modern cloud platforms (e.g., Azure ML, Vertex AI, SageMaker, Databricks)
- Design and maintain MLOps pipelines to support continuous integration, delivery, and monitoring of models
- Improve pipeline efficiency and reduce latency using techniques like model pruning and quantization
- Maintain best practices for model versioning, reproducibility, and performance optimization
- Establish effective observability, alerting, and cost monitoring for AI services
Integration & Collaboration
- Collaborate with data scientists to operationalize research models into stable, scalable services
- Partner with product engineering teams to integrate AI-driven features such as recommendations and personalization into the SaaS platform
- Work closely with DevOps, Security, and SRE teams to meet performance, compliance, and scalability standards
- Participate in architecture reviews and help shape AI systems through thoughtful, forward-looking guidance
What you’ll bring:
- 5+ years of experience in ML/AI or systems engineering, including 3+ years leading technical teams
- Demonstrated success operating production-grade ML systems at scale, ideally in a SaaS environment
- Understanding of multi-tenant SaaS architectures, real-time inference pipelines, and scalable model serving
- Hands-on experience working in Agile/Scrum teams, with a focus on team velocity, delivery, and continuous improvement
- Strong communication and collaboration skills, with the ability to lead across technical and non-technical teams
- A degree in Computer Science, Engineering, or a related field
Technical Skills & Expertise:
- Cloud ML Platforms: Azure ML, GCP Vertex AI, AWS SageMaker, Databricks
- ML Infrastructure Tools: MLflow, Kubeflow, Airflow, Feast (Feature Store), Docker, Kubernetes
- CI/CD & Ops: GitOps, ArgoCD, Terraform, Jenkins, GitHub Actions
- Monitoring & Observability: Prometheus, Grafana, OpenTelemetry, Datadog
- Programming Languages: Python (primary), Bash, optional: Go, Java, or Rust
- APIs & Services: REST, gRPC, Kafka/PubSub for data streaming
- Security & Compliance: IAM, RBAC, audit logging, model access controls
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.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Agile Airflow APIs Architecture AWS Azure CI/CD Computer Science Databricks DevOps Docker Engineering GCP GitHub Grafana Java Jenkins Kafka Kubeflow Kubernetes Machine Learning MLFlow ML infrastructure ML models MLOps Pipelines Python Research Rust SageMaker Scrum Security Streaming Terraform Vertex AI
Perks/benefits: Career development Flex hours Startup environment
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