
Applied Machine Learning Scientist (Early Career)
- Toronto, ON
- $120,000-150,000 per year
- Permanent
- Full-time
- Research & Productionise Prototype, validate, and deploy ML models that power search, ranking, recommendations, pricing, and conversational recommendation systems.
- Data Pipelines Build reliable pipelines in PySpark; ensure reproducibility, lineage, and monitoring.
- Experimentation Design online A/B tests, define success metrics, and analyse results to inform product decisions and areas of further experimentation.
- Tooling & Best Practices Contribute to internal ML libraries for training, evaluation, debugging, and interpretation; champion code quality and reproducibility.
- Research Awareness Stay current with ML/RL literature, and constantly evaluate new models
- Education Recently completed BSc, MSc, or PhD in Computer Science, Statistics, Mathematics, or a closely related technical field.
- Technical Skills
- Hands-on experience training, tuning, and debugging both classical models (e.g., GBDTs) and deep-learning models (primarily Transformers).
- Python proficiency and fluency with the scientific/ML stack: one of PyTorch / TensorFlow / JAX, core libraries (NumPy, Pandas, scikit-learn), and at least one gradient-boosting toolkit (XGBoost, LightGBM, or CatBoost).
- Strong command of algorithms, data structures, and object-oriented design.
- Applied ML Expertise
- Detecting & mitigating target leakage, train-test temporal skew, data drift, and other common pitfalls in building production models.
- Translate business objectives into quantifiable ML metrics (e.g., MRR, MAP, precision/recall, AUC, F1, NDCG) and choose appropriate loss functions (e.g., Plackett-Luce, cross-entropy, focal loss) to optimise them.
- High Agency & Ownership Demonstrated ability to identify opportunities, form hypotheses, and drive projects that align with business objectives with minimal supervision.
- Communication & Collaboration Clear written/verbal communication and a collaborative mindset.
- Reinforcement Learning Hands-on RL experience-especially fine-tuning LLMs toward verifiable objectives or applying RL/bandits in recommendation and ranking-is a strong plus.
- Domain Expertise Background in learning-to-rank, recommender systems, conversational agents, or NLP.
- Portfolio: Open-source contributions, Kaggle medals, blogs or peer-reviewed publications that replicate and extend academic research.
- Production ML Ops Experience with Spark, Airflow, Docker/Kubernetes, feature stores, and model observability/monitoring.
- Generous paid vacation + time off for your birthday
- Work from (almost) anywhere for up to 20 days per year
- Focus on mental health and well-being:
- Company-paid therapy sessions through SpringHealth
- Company-paid subscription to HeadSpace
- Company-wide week off a year - the whole team fully recharges (and returns without a pile-up of work!)
- Paid parental leave
- Paid volunteer time
- Focus on your career growth:
- Development Dollars
- Leadership development
- Access to thousands of on-demand e-learnings
- Travel Discounts
- Employee Resource Groups
- Private health and dental insurance
- Life and Disability insurance