Senior ML Engineer

2 weeks ago


New York, United States Next Ventures Full time

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) #J-18808-Ljbffr



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