Machine Learning Scientist

About the role

Remote Machine Learning Scientist owning applied ML from problem definition through validation and deployment. Work includes classification, entity resolution, anomaly detection, forecasting, risk assessment, graph methods, LLMs, RAG, knowledge graphs, reproducible evaluation, uncertainty assessment, and research-to-production collaboration. Requires PhD or exceptional recent PhD, applied ML, Python, PyTorch/sklearn, SQL, NLP/LLM retrieval, and rigorous experimental judgment. Preferred energy/climate domain, geospatial or graph ML.

Requirements

• PhD in machine learning, computer science, statistics, or related quantitative field; exceptional recent PhDs with substantial applied work considered
• 3–5 years applied ML experience or substantial recent PhD applied work
• Hands-on NLP/LLM applications, embeddings, RAG and evaluation
• Python, PyTorch/TensorFlow, scikit-learn, pandas, SQL
• Model validation, uncertainty, error analysis, reproducible research
• Deploying and evaluating models in real workflows

What you'll do

• Develop predictive models using text, geospatial, energy, market, media, and organizational data
• Use classical ML, deep learning, graph methods, LLMs, optimization, and scenario analysis
• Build evaluation benchmarks and assess uncertainty, calibration, drift, and model failure modes
• Build retrieval/synthesis systems and agents with systematic evaluation
• Use knowledge graphs and reusable data representations where useful
• Partner with engineering to deploy models and define monitoring/retraining

Where this was posted

• LinkedIn

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