Remote Machine Learning Engineer Jobs in North Carolina

Location

This position will be based in Charlotte, NC, New York, NY or Boston, MA.

Reporting

This position will report to the Director of Engineering, Building Science

Summary of Role

Come build the software and data layer that helps make energy cleaner and cheaper for millions of homes: turning fragmented, real-world energy data into structured, trustworthy intelligence that our products, customers, and partners can act on. You'll develop the services, pipelines, and models that estimate how homes use, produce, and pay for energy, and run them as reliable production APIs at scale. The role spans physics-based and machine-learning energy modeling, large-scale geospatial and remote-sensing data processing, utility and grid data processing across every US market, and AI systems where LLM-powered agents extract data from complex source documents with humans in the loop.

We're looking for a strong, versatile engineer, comfortable across frontend, backend, and the underlying infrastructure, and who is fluent with modern AI frameworks, tooling, and harnesses. This role sits in the Data and Energy Intelligence Unit, a multidisciplinary team of engineers and scientists owning data-intensive applications across energy modeling, geospatial data, and applied AI. Energy expertise is a plus, but we care most about excellent engineering and sound judgment; the domain is something a strong, curious engineer can learn on the job.

Strategic & Tactical

• Build and ship features end-to-end across the stack, from production APIs and data pipelines to the interfaces on top of them.
• Develop AI-powered capabilities where they fit, including LLM- and agent-based systems that extract structured, validated data from complex, unstructured documents with a human-in-the-loop review workflow.
• Take models from prototype to production: build the APIs, pipelines, and infrastructure that run physics-based and machine-learning models reliably at scale, with the testing and observability needed to catch regressions before they reach customers.
• Work with large geospatial and remote-sensing datasets (imagery, elevation, and related sources) that feed the platform's models.
• Raise the engineering bar and quality: lead and participate in peer technical design reviews, and hold a high standard for testing, observability, evaluation (including for nondeterministic AI outputs), and operational excellence.
• Communicate clearly with engineers, product partners, and non-technical stakeholders, while developing a deep understanding of the energy domain the platform serves.

Qualifications

Minimum:

• Advanced proficiency in Python for production software, with a demonstrated ability to write clean, maintainable, well-tested code and design solid APIs and services.
• Full-stack breadth: comfortable working across a backend and a modern frontend (e.g., React/TypeScript), and the underlying infrastructure. You are a generalist who can pick up whatever the problem needs.
• Hands-on experience building production LLM systems and agents with frameworks like pydantic-ai, LangGraph, or Claude/OpenAI Agent SDKs, including prompting, evals, and extracting structured data from messy, unstructured sources at scale.
• Fluency with agentic coding tools to multiply your impact, used critically: questioning and pressure-testing what they produce and keeping your own judgment in charge.
• Experience building and operating production systems: APIs and services, data pipelines, relational databases (SQL/PostgreSQL), containerization, cloud platforms (AWS/GCP/Azure), and observability, with sound practices (version control, code review, testing, CI/CD).

Preferred:

• Geospatial and remote-sensing experience: imagery, elevation/point clouds, and libraries such as rasterio, geopandas, and shapely; PostGIS.
• Depth in the modern Python web stack: FastAPI, SQLAlchemy, Pydantic, and async Python.
• Frontend depth: React, Vite, TypeScript, and data-fetching/state libraries (e.g., TanStack Query/Router).
• Strong quantitative or algorithmic aptitude, and comfort with the Python data stack (pandas, polars, NumPy).
• Experience in the clean energy space, with home energy data, or in building electrification and decarbonization.