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!

Benevity’s software architecture has evolved to include a diverse technology stack. The front-end application, using mainly VueJS, is designed for both desktop and mobile web rendering. Our back-end systems (some Java SpringBoot, some PHP) manage data processing, interfaces with external providers, and ensures robust security. We run and operate our systems in both GCP and AWS cloud, leveraging where possible cloud-native technology. We emphasize clean, maintainable code and use GIT for version control and collaboration. Additionally, our platform integrates with various external services for functionalities like email communication, content storage, and server-to-server interactions.

Our culture is driven by our core value of “we-are-we” and as a Senior AI Systems Engineer, you will work in an outcome-driven environment where collaboration with your product, design and engineering counterparts is paramount.

If you’re eager to make a difference and thrive in a collaborative setting, we invite you to join our team!

Position Overview

As a Senior AI Systems Engineer, you'll build and run the machine learning and GenAI systems that power Benevity's products. You'll turn promising prototypes into reliable, production-grade features that help impact leaders do more good. You'll own significant systems from design through monitoring, work closely with data scientists and product managers, and help raise the bar for the engineers around you. You'll work closely with the Staff engineers on architecture and strategy, and lead delivery in your own area.

What you’ll do

Design, build and operate ML and GenAI features end to end, from data ingestion and feature pipelines to model serving and monitoring.

Build LLM-powered applications such as search, assistants and agents, including RAG pipelines, tool integrations and multi-step workflows.

Create evaluation frameworks for GenAI systems that combine offline benchmarks, online metrics and human review.

Implement guardrails for GenAI applications, including prompt injection defenses, hallucination mitigation and responsible AI practices.

Put MLOps standards into practice: versioning, automated testing, A/B testing, drift detection and safe rollbacks.

Translate business problems from product managers and data scientists into clear technical requirements and scoped delivery plans.

Write design docs, take part in design reviews and give thoughtful code reviews.

Mentor mid-level engineers on production ML practices and system design.

Balance latency, cost and quality when choosing models, prompts and infrastructure.

Find and fix bottlenecks in pipelines, serving and platform performance.

What you’ll bring

Bachelor's or Master's degree in Computer Science, Mathematics or a related field, or equivalent professional experience.

5+ years of software engineering experience, including 3+ years building and deploying ML models in production.

Hands-on experience across the ML lifecycle: training, evaluation, deployment, monitoring and retraining.

Strong Python skills and experience with ML frameworks such as PyTorch, TensorFlow or scikit-learn.

Experience with at least one major cloud platform (AWS, Azure or Google Cloud) and with distributed workloads.

Working knowledge of data pipelines, feature engineering and large-scale data processing.

Familiarity with ML tooling such as experiment tracking, model registries, feature stores or orchestration frameworks.