Data Scientist
TypeFull-time job
LocationUnited States
Posted2 hours ago
This is a remote position.
About Monaire
Monaire is building the infrastructure layer for intelligent commercial HVAC. We combine on-device sensors, smart thermostats, and machine-learning systems to automate control, surface real operational insight, and materially reduce energy waste at scale. This is not offline modeling or notebook ML. Models run in production, interact with physical systems, and must be observable, debuggable, and correct. The platform spans edge devices, cloud services, streaming pipelines, control logic, and ML inference.
Engineers here work on:
Data ingestion and streaming at scale from heterogeneous hardware
Low-latency decision pipelines and control loops
ML systems that survive missing data, drift, and adversarial real-world conditions
Infrastructure for model deployment, monitoring, and rollback
Apps and services that customers depend on to run their buildings every day
The market is large, broken, and technically underserved. We’re scaling the system and need engineers who care about correctness, performance, and ownership — people who want to build infrastructure that actually controls the physical world, not just dashboards that look good in demos.
Role Overview
As aData Scientist / Senior Data Scientist, you will play a critical role in buildingproduction-grade ML systemsthat drive real-world outcomes—energy efficiency, predictive maintenance, anomaly detection, and operational intelligence for HVAC/R systems.
You will work closely withbackend engineers, product managers, and domain expertsto translate raw sensor data into reliable models that power customer-facing features and internal decision-making.
This role requires someone who canthink long-term architecturally, while deliveringshort-term, measurable impactin a fast-moving startup environment.
What You'll Do:
Scale ML systems for 5X growth—optimize batch processing, database queries, and model inference
Design ML models for time-series data, anomaly detection, and predictive maintenance
Optimize production systems:
About Monaire
Monaire is building the infrastructure layer for intelligent commercial HVAC. We combine on-device sensors, smart thermostats, and machine-learning systems to automate control, surface real operational insight, and materially reduce energy waste at scale. This is not offline modeling or notebook ML. Models run in production, interact with physical systems, and must be observable, debuggable, and correct. The platform spans edge devices, cloud services, streaming pipelines, control logic, and ML inference.
Engineers here work on:
Data ingestion and streaming at scale from heterogeneous hardware
Low-latency decision pipelines and control loops
ML systems that survive missing data, drift, and adversarial real-world conditions
Infrastructure for model deployment, monitoring, and rollback
Apps and services that customers depend on to run their buildings every day
The market is large, broken, and technically underserved. We’re scaling the system and need engineers who care about correctness, performance, and ownership — people who want to build infrastructure that actually controls the physical world, not just dashboards that look good in demos.
Role Overview
As aData Scientist / Senior Data Scientist, you will play a critical role in buildingproduction-grade ML systemsthat drive real-world outcomes—energy efficiency, predictive maintenance, anomaly detection, and operational intelligence for HVAC/R systems.
You will work closely withbackend engineers, product managers, and domain expertsto translate raw sensor data into reliable models that power customer-facing features and internal decision-making.
This role requires someone who canthink long-term architecturally, while deliveringshort-term, measurable impactin a fast-moving startup environment.
What You'll Do:
Scale ML systems for 5X growth—optimize batch processing, database queries, and model inference
Design ML models for time-series data, anomaly detection, and predictive maintenance
Optimize production systems:
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