Senior Software Engineer, Machine Learning

Job summary

Own the end-to-end Automated Personalized Marketing product, including the training, deployment, and monitoring of ML models. Collaborate with the founding team to translate business requirements into production-ready ML systems and integrate them with the data platform.

Job details

Senior Software Engineer, Machine Learning Chorus · Toronto, Ontario Location: Toronto, Ontario, in office ~4 days/week (68 Claremont Street, near Ossington and Queen St. West) Compensation: 150,000–170,000 CAD + equity Reports to: Technical CEO, working directly with our Founding Engineer and Founding Data Scientist Apply here: About Chorus Chorus is the marketing intelligence platform for nonprofits. We help mission-driven organizations send the right message to the right supporter at the right moment, building a preference profile for every supporter and routing each one to the content they're most likely to act on. Nonprofits that use Chorus raise 10–20% more revenue from their marketing. We work directly with nonprofits and advocacy organizations in the US and Canada, and alongside some of the sector's leading marketing agencies, reaching campaigns and programs with budgets in the billions, up to and including U.S. presidential campaigns. Our customers include Feeding America and the Human Rights Campaign. We closed a seed round earlier this year and are seeing early product-market fit: we're closing tens to hundreds of thousands of dollars in new revenue every month. Now we need an experienced engineer to own the product that's driving it. The role This is our third non-founder hire. You'll work out of our Toronto office alongside our technical CEO, Founding Data Scientist, and Founding Engineer. You'll own Automated Personalized Marketing, our product that uses machine learning models to build audience segments in real time. That means three things: The ML product, end to end. You'll own the system clients use every day: how models are trained, deployed, served, and monitored, and how their output becomes segments a marketer can act on. You own its reliability, and its performance. The path from model to production. You'll work closely with the founding data scientist to translate business requirements into machine learning definitions and model designs. You'll then train the respective models and turn them into production systems, with evaluation that reflects how the models are actually used, not just how they scored offline. The platform underneath. Our Founding Engineer owns the data platform that feeds it all: ingesting client marketing data, keeping it clean and current, and making it fast enough to segment in real time. You'll collaborate closely with them, shaping what the platform needs to deliver for the ML product and building on top of it. What you'll bring 5+ years of relevant software engineering experience, with a track record of owning production systems end to end. Hands-on ML engineering. You've trained machine learning models, deployed them to production, and evaluated how they perform once real users and real data hit them. Strong engineering fundamentals: testing, CI/CD, observability, and the discipline to keep a small team's codebase healthy as it grows. Comfort across our backend stack: a Python/FastAPI backend, MongoDB, S3, Redis, Pinecone, and AWS, deployed on Render. Comfort across our emerging data platform stack: Python and DuckDB on Dagster Judgment to work independently in an early-stage environment. You set your own direction on ambiguous problems, and you know when something is ready to ship. Numerical computing and performance optimization at scale. You're comfortable with the linear algebra under our models, including large matrix operations, and you know how to make it fast and memory-efficient on big datasets: vectorization, sparse representations, batching, and profiling to find the real bottleneck. Bonus points Martech, CRM, or marketing data experience (email engagement, supporter or donor behavior). Experience with hexagonal (ports-and-adapters) architecture. It's how our FastAPI backend is structured. Experience building retrieval or embedding-based systems. We use Pinecone for embedding-based matching in our models. C$150,000 - C$170,000 yearly

What you’ll do

Own the end-to-end Automated Personalized Marketing product, including the training, deployment, and monitoring of ML models. Collaborate with the founding team to translate business requirements into production-ready ML systems and integrate them with the data platform.

Requirements

Requires 5+ years of software engineering experience with a proven track record of deploying production ML systems. Must be proficient in Python, AWS, and numerical computing, with the ability to work independently in an early-stage environment.

Benefits

• Equity

Listed skills

• CI/CD · Preferred
• Redis · Preferred
• Amazon Web Services · Preferred
• MongoDB · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Machine Learning Engineering
• Python
• FastAPI
• MongoDB
• S3
• Redis
• Pinecone
• AWS
• Render
• DuckDB
• Dagster
• CI/CD
• Numerical Computing
• Performance Optimization
• Linear Algebra
• Hexagonal Architecture

Job areas

• Software
• Technology
• Data & Analytics
• Engineering
• Marketing

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