Machine Learning Scientist for Decarbonization AI (Remote)

GoodPower; United States; Remote; USD 160,000 - 190,000; Full time

Benefits offered by this job

Medical insurance

Vacation time (2–4 weeks)

Sabbatical after 5 years

Parental leave

Education reimbursement

Retirement plan (403(b) with match)

Wellness stipend

Job summary

GoodPower seeks a Machine Learning Scientist to expand what our teams and software can accomplish through applied research, predictive modeling, and intelligent automation.

You will identify where AI/ML can improve decisions, unlock new offerings, and reduce time and cost of delivering work across clean energy planning, public participation integrity, media and narrative intelligence, and organizational effectiveness.

Qualifications

• PhD in machine learning, computer science, statistics, or related quantitative field with 3–5 years of applied ML experience.
• Evidence of turning ambiguous problems into rigorous experiments and useful AI/ML capabilities.
• Hands-on experience with NLP and LLM applications, embeddings, and retrieval-augmented generation.

Responsibilities

• Translate user needs into tractable ML problems and define success criteria.
• Develop models for classification, forecasting, and anomaly detection across diverse data.
• Evaluate models using calibration, uncertainty, and robust testing across contexts.
• Build retrieval/synthesis systems and evaluate tool-assisted workflows.

Skills

NLP and LLM applications

Python

PyTorch/TensorFlow

scikit-learn

Pandas

SQL

Version control

Statistical judgment

Communication

Experiment design

Education

PhD in machine learning, computer science, statistics, or related quantitative field

Tools

PyTorch

TensorFlow

scikit-learn

Pandas

SQL

Job description

GoodPower seeks a Machine Learning Scientist to expand what our teams and software can accomplish through applied research, predictive modeling, and intelligent automation.

You will identify where AI/ML can improve decisions, unlock new offerings, and reduce time and cost of delivering work across clean energy planning, public participation integrity, media and narrative intelligence, and organizational effectiveness.