Machine Learning Engineer
- Toronto, ON, Canada
- Greater Toronto Area, Canada, Canada
Full-time
Applications open in a new tab.
Job details
- Job type
- Full-time
- Apply by
- Posting language
- English
- Posted
Original job description
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
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.
- 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
- 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.
- Forecasting, causal inference, or recommender systems
- Synthetic data or simulation
- Privacy-preserving or multi-tenant machine learning
- Experience with LLM fine-tuning or embeddings
- 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
More about this job
Responsibilities
Build and deploy machine learning models for digital twins, including forecasting models that translate campaign responses into demand predictions. Develop customer data and model-training pipelines, evaluate model fidelity, and ensure customer data remains isolated while models improve.
Requirements
Applicants should have experience training and shipping production machine learning models, strong Python and modern machine learning skills, and rigor in evaluation and statistical validity. They must be able to explain model behavior to engineers and customers and be based in Toronto or willing to relocate.
Skills
- Machine Learning
- Python
- Model Training
- Model Evaluation
- Model Deployment
- Forecasting
- Data Pipelines
- Feature Engineering
- Statistical Analysis
- Causal Inference
- Recommender Systems
- Synthetic Data
- Simulation
- Privacy-Preserving Machine Learning
- LLM Fine-Tuning
- Embeddings
Benefits
- Annual Learning Budget
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Engineering
- Science & Research
Keywords
- Machine Learning
- Python
- Digital Twins
- Forecasting
- Demand Prediction
- Data Pipelines
- Data Warehouses
- Feature Engineering
- Model Training
- Model Evaluation
- Model Deployment
- Statistical Validity
- Causal Inference
- Recommender Systems
- Synthetic Data
- Simulation
- Privacy-Preserving Machine Learning
- Multi-Tenant Machine Learning
- LLM Fine-Tuning
- Embeddings
- AI Agents
- Campaign Analytics
- Production Machine Learning