Machine Learning Engineer, Growth Platform

What you'll do

• Design, train, evaluate, and deploy models for recommendation, ranking, and personalized action selection across various platforms.
• Improve contextual bandit and policy-learning approaches to adapt recommendations based on user context and feedback.
• Build agent-based recommendation capabilities that utilize business context to identify relevant products and integration options.
• Develop reliable data and feature pipelines to ensure high quality and consistency between training and production.
• Create reusable tooling for model evaluation, retraining, and safe rollouts to accelerate the deployment of improvements.
• Maintain production ML components by writing tested code and monitoring models for reliability, latency, and cost.
• Design and analyze online experiments to connect offline evaluations to measurable product adoption and business impact.
• Collaborate with cross-functional partners to identify problems solvable by shared machine learning capabilities.

What we're looking for

• 3+ years of industry experience in machine learning engineering, software engineering, or applied data science.
• Hands-on experience building and shipping ML models in production.
• Strong programming skills in Python and experience writing maintainable, tested production code.
• Practical experience designing, training, and evaluating ML models using PyTorch, TensorFlow, XGBoost, or scikit-learn.
• Experience building data or feature pipelines with proficiency in SQL and distributed tools like Spark or PySpark.
• Strong understanding of statistics, model evaluation, experimentation, and identifying data leakage.
• Experience deploying, monitoring, and debugging production ML systems while balancing quality, reliability, latency, and cost.
• Ability to translate open-ended business problems into technical approaches and collaborate across cross-functional teams.
• Experience with recommendation systems, ranking, personalization, or marketplace/advertising optimization (preferred).
• Experience with contextual bandits, policy learning, causal inference, or off-policy evaluation (preferred).
• Experience building and evaluating LLM applications including structured extraction and embeddings (preferred).
• Experience building reusable ML capabilities like training automation or feature systems (preferred).
• Experience with product growth, lifecycle messaging, or balancing engagement with long-term outcomes (preferred).