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Smart Buildings & Smart Cities
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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.
As a Data Scientist / Senior Data Scientist, you will play a critical role in building production-grade ML systems that drive real-world outcomes—energy efficiency, predictive maintenance, anomaly detection, and operational intelligence for HVAC/R systems.
You will work closely with backend engineers, product managers, and domain experts to translate raw sensor data into reliable models that power customer-facing features and internal decision-making.
This role requires someone who can think long-term architecturally, while delivering short-term, measurable impact in 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: <3s response times, 30% cost reduction, 99.9% uptime
Database optimization (MongoDB): indexes, connection pooling, 3-5X performance improvement
Batch processing: parallel processing, async operations, memory management
Model optimization: <500ms inference latency, caching strategies
NLP & LLM: enhance conversational AI bots with intelligent query generation
Build monitoring systems: real-time dashboards, SLA tracking, automated scaling
2+ years hands-on data science/ML experience
Strong Python (NumPy, Pandas, Scikit-learn)
Deep learning: TensorFlow, Keras, or PyTorch
MongoDB: Query optimization, indexing, aggregation pipelines
Database optimization: Index design, query tuning
Batch processing: Parallel processing (multiprocessing/async)
Time-series data, anomaly detection, statistical modeling
Strong CS fundamentals and debugging skills
MLOps tools, Lambda optimization, caching (Redis/ElastiCache)
Monitoring: Grafana, Prometheus
NLP/LLM: Prompt engineering, conversational AI
IoT/sensor data experience, startup experience
AWS: Lambda, S3, CloudWatch, ElastiCache/Redis
Docker, SQL, Flask API development
Qualifications
Bachelor's/Master's/PhD in CS, IT, Applied Math, Statistics, or related field
Competitive salary + equity with meaningful ownership
Comprehensive health insurance (self, spouse, children, and parents)
Remote-first, flexible work culture
Opportunity to work on high-impact systems with climate and sustainability impact
Strong emphasis on engineering excellence, ownership, and growth
Collaborative, inclusive, and low-ego team culture
After you apply, unlock the direct contact details of the people who actually make the call. A quick follow-up makes you 5x more likely to land an interview.
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