Full-Time Machine Learning Engineer

• Work with data scientists to deploy causal inference models for online marketing.
• Design and maintain machine learning pipelines that support model deployment.
• Collaborate with product managers, engineers, and data scientists to turn experimental models into working systems.
• Monitor model performance, accuracy, and speed, and make regular updates.
• Automate routine tasks and build systems that can handle large amounts of data.
• Document work and share technical details and results with both technical and non-technical team members.

Skills:

• Engineer and maintain production-grade machine learning systems.
• Leverage Python and industry-standard ML frameworks like TensorFlow, PyTorch, and Scikit-learn.
• Design and orchestrate end-to-end ML workflows using tools such as MLflow and Apache Airflow.
• Develop and deploy containerized microservices using Docker and Kubernetes.
• Apply causal inference techniques—including A/B testing, difference-in-differences, and propensity score matching.
• Work hands-on with cloud platforms like AWS and big data technologies such as Apache Spark, Dask, or Polars.

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