Machine Learning Engineer

What you'll do

• Build, train, deploy, and monitor production models for pricing, automation, and decision systems; own their pipelines end to end.
• Turn research prototypes into production-ready systems and build services, APIs, and platforms that support the ML lifecycle.
• Address challenges such as data sparsity, drift, and model decay; lead design reviews and mentor teammates.

What they ask for

• Senior-level or above experience shipping and operating production ML systems, ML-adjacent services, or data/ML platforms.
• Strong Python fundamentals and proficiency in statistics, including reasoning distributionally and monitoring ML systems.
• Expertise across the ML lifecycle: training, evaluation, deployment, monitoring, and iteration.
• Able to communicate design decisions and tradeoffs; based in or willing to relocate to Miami, Toronto, or Seattle.

Nice to have

• Experience in business-critical ML domains such as pricing, forecasting, logistics, marketplaces, or risk.
• Experience with streaming or event-driven systems, Kafka, gRPC, Redis, or workflow engines.

Tools

Python, MLflow, Airflow, Spark, Delta Lake, Kafka, gRPC, Redis

AI focus

Build ML models and platforms for real-estate decision automation, including agentic research and automated retraining; use AI tools daily and help advance their use.