Senior Machine Learning Engineer

5+ years of full-time software engineering experience, including at least 3 years working on ML systems
Deep knowledge of modern machine learning algorithms (tree-based methods, deep learning architectures, transformers/LLMs)
Hands-on experience with PyTorch, TensorFlow, XGBoost or equivalent frameworks
Track record building production-scale ML infrastructures, ideally using GCP (Vertex AI, KubeFlow, BigQuery, etc.)
Familiarity with CI/CD, containerization (Docker/Kubernetes), and distributed training (Spark, Ray, Dask, etc.)
Strong proficiency in Python (numpy, pandas, etc.)
Experience with scalable data processing (Spark, Ray, BigQuery)
Comfortable with advanced experimentation techniques
Understanding of performance measurement in real-world deployments
Excellent communication—able to explain complex ML concepts to non-technical stakeholders
Self-starter mentality with the ability to own projects from ideation to deployment, picking up and learning new technologies as needed

Design, train, and deploy models that directly power our marketing-focused products, primarily for marketing use cases
Architect and maintain robust, scalable, MLOps pipelines to ensure reliable training, serving, and monitoring of models in production
Drive continuous improvement using A/B testing, uplift modeling, causal inference, and other advanced experimentation frameworks to validate and refine model performance
Work closely with cross-functional teams, including the CTO and Director of Data, to align on product goals and foster best practices for machine learning and data engineering across the organization

Familiarity with marketing technology or ads is a strong plus
Experience with experimental design and methods such as causal inference or uplift modeling
Exposure to modeling with LLMs and modern AI tooling
Productionizing Reinforcement Learning and Bandit algorithms
Ph.D in a technical field
Experience in a fast-paced or startup environment
You live in or near New York City.