Machine Learning Engineer

About the Role — Senior and Above

We’re looking for senior and above ML engineers to build, ship and scale product‑impacting machine learning systems  role lies across the entire ML stack: from model building and intelligent services to platform engineering that accelerates model learning. You’ll work directly with researchers, product and operations teams to deliver automation that scales in real‑world, unstructured real‑estate environments.

• Build and train models that influence real customers and real money, including pricing, automation and decision systems in production.
• Turn prototypes into clean, testable, production‑ready code and systems, working side‑by‑side with researchers and analysts.
• Own model pipelines end‑to‑end: data ingestion, training, validation, versioning, deployment, and monitoring.
• Design, build and evolve mission‑critical services and APIs that connect to real‑world, messy operations.
• Build the platform that accelerates the full ML lifecycle: agentic research, automated retraining, experimentation, deployment and monitoring.
• Proactively tackle real‑world challenges such as sparsity, data drift and model decay in a volatile market.
• Use AI tools daily and help push them further than anyone else in the industry.
• Lead technical design reviews, mentor teammates and raise the bar on everything around you.

• You ship. You pick the boring solution when boring is right and the novel one when it isn’t.
• You have high agency and take ownership of problems end‑to‑end without waiting for permission.
• You run at unclear problems and see ambiguity as an opportunity, not an obstacle.
• You hold a high standard: you review code, raise the bar on everything around you and focus on end‑to‑end judgment.
• You think in first principles and have opinions on architecture, distributed systems, ML lifecycle tradeoffs and operating constraints in a high‑stakes environment.
• You default to AI and have already integrated modern AI tools into your workflow to move faster.
• You communicate well, write clear design docs, give useful code reviews, push back on bad ideas without personalizing, and can land technical tradeoffs with non‑technical stakeholders.
• You believe in what we’re building and have conviction, seeing opportunity in what we do.
• You have fun and stay human even when times are hard.

• Senior‑level or above: deep experience shipping and operating production ML systems, ML‑adjacent services, or data/ML platforms.
• Strong fundamentals in Python and comfortable picking up new languages.
• Proficiency with statistics and ability to reason distributionally, with real‑world monitoring experience.
• Expertise with the end‑to‑end ML lifecycle (training, evaluation, deployment, monitoring, and iteration) and associated tooling (e.g., MLflow, Airflow, Spark, Delta Lake).
• Demonstrated ability to make and communicate design decisions and tradeoffs across stakeholders.
• Based in or willing to relocate to Miami, Toronto, or Seattle.

• ML systems experience in business‑critical domains such as pricing, forecasting, logistics, marketplaces, or risk.
• Streaming and event‑driven systems experience (e.g., Kafka), gRPC, Redis, or workflow engines.
• Interest in real estate or other messy, high‑stakes domains with imperfect data.