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Salary: USD 131,000 - 188,000 / annual
Arcadia is an AI-powered energy intelligence platform for enterprise customers. The company helps Fortune 2000 companies manage utility bills, procure energy, and advance sustainability at scale through three core solutions: utility bill management automation, energy procurement advisory powered by AI and market expertise, and verified emissions reporting.
You will join the Applied AI team within R&D—a small, delivery-focused group that ships the machine learning and AI systems powering Arcadia's utility data platform. This is a hands-on role where you will own end-to-end delivery of one or more core workstreams: utility bill data extraction, forecasting, or audit/anomaly detection. You will work closely with engineering partners and the Director of Applied AI, taking ownership of ambiguous problems, setting technical approaches where none exists yet, and helping shape how the team sequences its work.
Key responsibilities include:
- Own delivery of core workstreams (bill data extraction, forecasting, or audit/anomaly detection) and flex across them as priorities shift
- Build, evaluate, and improve production models: document classifiers and extraction agents, forecasting models for bill availability and spend, and detection systems for audits and controls
- Set the technical approach on novel problems—investigate, decide, and carry recommendations through to shipped results
- Partner with the Director on prioritization and sequencing, bringing strategic perspective rather than only execution capacity
- Write production-grade Python and contribute to shared codebase, pipelines, and design documentation
- Partner with engineering on model integration, monitoring, and post-deployment behavior as the authority on modeling approach
- Automate manual steps in your own and the team's workflow using AI tooling, and share techniques that raise throughput
You will work in a remote-first culture with flexible PTO, 11 annual holidays, 10 days sick leave, parental leave benefits, and 75-95% employer cost coverage for medical, dental, and vision benefits.
Requirements:
- At least 3 years of hands-on experience building and evaluating production ML models, with the judgment to make progress on ambiguous problems independently
- Strong ML fundamentals: statistics, classification, regression, and evaluation methodology
- Fluency in Python (pandas, scikit-learn, numpy) and SQL; comfort working in live production codebases, not just notebooks
- Practical experience with LLMs or agentic workflows in production
- Comfort with imperfect ground truth and managing inherent uncertainty in data
- Track record of driving work with minimal oversight: forming and documenting assumptions, making technical calls, and standing behind recommendations
- Clear communication that adapts to audience: able to explain model behavior to engineers and non-technical partners
- Bachelor's or Master's in CS, Statistics, Math, or related field (or equivalent experience), plus a portfolio of past work (GitHub, papers, or project write-ups)
