Machine Learning Engineer

Who We Are

We are a team building the infrastructure for the agent era. A decade ago, the brands that leaned into mobile won. The same shift is happening with agents: how people find businesses, ask questions, and buy is moving from static pages into conversations. 800 million people already discover brands through agent search, and who gets chosen is being decided right now.

The Team

Make Point11's digital twins more faithful to the customers they stand in for. You own the models and data behind them: how a twin is built from a company's own records, how we measure that it behaves like the real customer, and how it improves with every campaign. Every interaction has to feel intelligent, and it has to happen in .11 seconds. Small team, high bar. Founded by ex-Googlers. We hire accordingly.

What Your First Year Looks Like

• Measure how closely a customer's digital twins predict real outcomes, then close the gap.
• Build the forecasting models that turn a campaign's answers into demand a brand can plan around.
• Design the training and evaluation pipeline that lets every customer's twins improve without leaking one company's data into another's.

What the Day-to-Day Feels Like

• Own modelling for digital twins: features, training, evaluation, and deployment
• Build data pipelines from customer CRMs and warehouses into model-ready form
• Run experiments with clear hypotheses and honest results
• Partner with AI Engineers on where models end and LLM agents begin
• Take models to production and own their quality there

About You

• You've trained and shipped machine learning models that run in production
• You're strong in Python and the modern ML stack
• You're rigorous about evaluation, baselines, and statistical validity
• You can explain a model's behavior to an engineer and to a customer
• You're in Toronto or willing to relocate. This role is hybrid, in our Toronto office three days a week. This posting is for an existing vacancy.

Even Better If You Have

• Forecasting, causal inference, or recommender systems
• Synthetic data or simulation
• Privacy-preserving or multi-tenant machine learning
• Experience with LLM fine-tuning or embeddings

What We Offer

• Your models decide how faithful our product is
• Hybrid in Toronto, in New York's time zone
• A founding role in Point11's Canadian team
• A direct line to the founding team on product
• An annual learning budget for conferences and development