Lyft is seeking to improve ad relevance, targeting, optimization, and measurement algorithms to power the Lyft Ads platform and drive meaningful revenue growth
Requirements
- Strong proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, JAX, or scikit-learn; ability to write clean, efficient, production-adjacent code.
- Experience working with large-scale datasets and distributed data tools (Spark, Snowflake, Presto, Databricks).
- Practical experience building and evaluating: Ranking and relevance models, Optimization or pacing algorithms, Predictive models for CTR, CVR, or user response, Causal or experimentation-based measurement methods
- Understanding of online/offline evaluation techniques, including: Offline metrics (AUC, NDCG, MRR, calibration), A/B testing methodologies, Bias correction and counterfactual estimation
- Ability to solve ambiguous problems by structuring analyses, evaluating trade-offs, and proposing algorithmic solutions grounded in scientific rigor.
- Demonstrated ownership of modeling work, including debugging, monitoring, documentation, and iteration after deployment.
- Curiosity, initiative, and a track record of delivering measurable improvements through high-quality modeling.
Responsibilities
- Design, develop, and deploy production-grade machine learning models and algorithms that power core Lyft Ads capabilities, such as ad relevance, targeting, ranking, bid optimization, pacing, campaign delivery, and measurement.
- Own the end-to-end lifecycle of modeling projects — including problem definition, data exploration, feature engineering, model development, offline evaluation, deployment, and monitoring.
- Collaborate closely with Ads Engineering to integrate models into real-time ad-serving and batch decision systems, ensuring performance across latency, scalability, and reliability constraints.
- Analyze large-scale mobility, behavioral, and ads performance datasets to identify patterns, surface opportunities, and guide ML and AI driven product improvements.
- Implement rigorous model evaluation frameworks, including offline metrics, statistical tests, calibration, sensitivity analysis, and A/B experimentation to validate both model impact and system-level outcomes.
- Build robust training pipelines, feature transformations, and scoring infrastructure, ensuring reproducibility, observability, and long-term maintainability.
- Partner with Product, Engineering, and Sales to translate ambiguous advertiser goals (e.g., increased conversions, reach efficiency, brand lift) into measurable requirements and success metrics.
Other
- Master’s, or PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, Engineering, or related quantitative fields; or equivalent applied industry experience.
- 3–5 years of hands-on ML/applied science experience, ideally involving production models, large-scale systems, or ads/recommendation/relevance domains.
- Strong communication skills, with an ability to clearly explain model behavior, constraints, trade-offs, and recommendations to engineering, product, and sales partners.
- In-office work on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays.
- Hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year.
