Remote ML Engineer - Climate Policy & Energy Solutions

Planet Reimagined; New York (NY); Remote; USD 120,000 - 150,000; Full time

Job summary

Planet Reimagined is seeking a Machine Learning Engineer / Data Scientist to design and build data systems accelerating climate solutions across local and federal scales. You will create LLM-powered knowledge graphs, pipelines, and tools to extract policy elements and model climate interventions.

Join a mission-driven team focused on policy-informed energy optimization, transparency, and scalable climate action. Strong data, ML, and collaboration with policymakers are essential for this role.

Qualifications

• 3-5 years of experience in data science and ML engineering, preferably in GCP/AWS/Azure ecosystem
• Strong proficiency in LLM application design, context engineering strategies, Python, and SQL
• Hands-on experience building functional knowledge systems for LLM applications
• Proven ability to work with varied, unstructured data sources including municipal records and reports, news coverage, video documentation, and government datasets
• Strong problem-solving skills and ability to thrive in ambiguous climate policy and energy environments
• Excellent communication skills for cross-functional collaboration with policy experts, community stakeholders, and federal planners including turning data analysis into data storytelling outputs.

Responsibilities

• Build LLM-powered knowledge graphs, wikis, and structured datasets from policy documents, municipal records, community sentiment, and energy data
• Develop LLM and NLP pipelines that extract policy elements, resolve entities, and link them across sources
• Track data provenance so every policy element traces back to its source document, passage, and extraction method
• Design human-in-the-loop workflows where policy experts validate extracted policy elements and corrections improve models
• Construct policy timelines by extracting and ordering events to show how climate solutions unfold over time
• Design, train, and deploy models for policy effectiveness scoring, solution replicability, and energy development potential
• Build recommendation and graph-based models that match proven climate policies to a specific city, county, or community
• Own the full modeling lifecycle: baselines, feature engineering, model selection, tuning, and error analysis
• Develop evaluation frameworks to measure model, extraction, and LLM output quality against expert-validated ground truth
• Analyse community-sentiment data to quantify support for climate policies within surveyed localities
• Build reproducible training, evaluation, and deployment pipelines with experiment tracking, versioning, and a model registry
• Automate CI/CD for models, with validation gates and staged rollouts
• Maintain the data pipelines and API integrations (BigQuery, Cloud Functions) that feed models from government, community, and federal data sources
• Monitor model performance, data drift, and knowledge graph quality, with data governance for sensitive municipal data and PII.

Skills

LLM application design

Python

SQL

Data engineering

Problem solving

Tools

BigQuery

Pandas

NumPy

dbt

Apache Beam/Dataflow

Cloud Functions

Job description

Planet Reimagined is seeking a Machine Learning Engineer / Data Scientist to design and build data systems accelerating climate solutions across local and federal scales. You will create LLM-powered knowledge graphs, pipelines, and tools to extract policy elements and model climate interventions.

Join a mission-driven team focused on policy-informed energy optimization, transparency, and scalable climate action. Strong data, ML, and collaboration with policymakers are essential for this role.