Applied Machine Learning Scientist

Summary

Join OpenTable's team as a remote ML Scientist, initially working remotely with a future transition to a hybrid model in downtown Toronto. This role offers the chance to work on impactful projects, deploying ML models for search, ranking, recommendations, and more. You will build data pipelines, design A/B tests, and contribute to internal ML libraries. The ideal candidate possesses strong ML skills, experience with various models and tools, and a proven ability to translate business objectives into quantifiable metrics. OpenTable provides a collaborative environment and offers various benefits, including generous paid time off, mental health support, and career growth opportunities. Visa sponsorship is not available at this time.

Requirements

• Recently completed BSc, MSc, or PhD in Computer Science, Statistics, Mathematics, or a closely related technical field
• 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

Responsibilities

• 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

Preferred Qualifications

• 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

Benefits

• 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