Senior ML Engineer

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)

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