Applied Machine Learning Scientist, OpenTable

• 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
• 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
• Demonstrated ability to identify opportunities, form hypotheses, and drive projects that align with business objectives with minimal supervision
• Clear written/verbal communication and a collaborative mindset
• Portfolio: Open‑source contributions, Kaggle medals, blogs or peer‑reviewed publications that replicate and extend academic research

Desirable

• OpenTable seats 25 million diners each month across 60,000+ restaurants and taps into more than 20 years of booking data—an ideal launchpad for an early‑career ML scientist
• In our tight‑knit team, every experiment you run and every model you ship goes live quickly and at scale
• You’ll start with well‑scoped projects and close mentorship, then rapidly earn the freedom to pitch and implement research ideas that deliver measurable value for diners, restaurants, and the business
• We celebrate agency, speed, and relentless experimentation, balanced by disciplined prioritization rooted in ML expertise and real‑world production and business constraints
• 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