About the Role We’re looking for a Senior Machine Learning Engineer to help build and scale the models and infrastructure behind a high-impact data platform used by a wide range of customers. You’ll work on end-to-end machine learning systems—from experimentation and model development to deployment, serving, and ongoing optimization. This is a hands-on role where you’ll collaborate closely with leadership and engineering teams to shape the future of the product. What You’ll Do • Build and productionize ML models that directly power core product features • Design, maintain, and scale ML infrastructure including training pipelines, model serving, and monitoring • Run experiments and optimizations using A/B testing, uplift modeling, and causal inference methods • Collaborate cross-functionally with product and engineering, including direct work with senior leadership • Mentor teammates and help establish best practices across ML, data engineering, and experimentation Who We’re Looking For Experience & Expertise • 5+ years of software engineering experience, including 3+ years working on ML systems • Strong understanding of modern ML techniques (tree-based models, deep learning, transformers, etc.) • Hands-on experience with frameworks such as PyTorch, TensorFlow, or XGBoost • Experience with feature engineering using aggregations, embeddings, or auxiliary models MLOps & Infrastructure • Experience designing ML pipelines and production-grade infrastructure • Familiarity with cloud platforms (GCP preferred but not required) • Comfort with CI/CD, Docker/Kubernetes, and distributed compute frameworks (Spark, Ray, Dask, etc.) • Proven track record iterating on models in production environments Software Engineering Skills • Strong Python skills (numpy, pandas, etc.) • Experience with large-scale data processing (Spark, Ray, BigQuery, etc.) • Familiarity with workflow orchestration tools like Airflow Analytical & Experimental Skills • Comfort with advanced experimentation techniques and real-world performance evaluation • Understanding of observational data challenges and measurement frameworks Soft Skills & Culture • Comfortable owning projects end-to-end—from data exploration through deployment • Ability to communicate complex ML concepts clearly to technical and non-technical stakeholders • A self-starter who learns quickly and thrives in an iterative, fast-paced environment Bonus Points • Experience working with customer-facing or personalization-oriented ML systems • Background in causal inference or uplift modeling • Exposure to LLMs, modern AI tooling, or reinforcement learning • Advanced degree in a quantitative field • Experience in fast-moving or startup environments • Based in or near New York City (most of the team operates in EST)