Applied AI/ML Engineer
$200,000–$250,000 year
On-siteNew York City, New York, United States
Job Summary
Build LLM systems that transform complex, inconsistent financial documents into clean, validated data and design agentic workflows with retrieval and reasoning capabilities for fragmented enterprise systems. Wrangle messy, real-world data through baselining, anomaly detection, and entity reconciliation across heterogeneous sources, while developing demand forecasting models using classical time-series and gradient-boosted approaches with rolling-origin backtesting. Own end-to-end AI problem ownership from research and prototyping to production deployment, including evaluation harnesses, guardrails, and monitoring for probabilistic systems. Manage eval datasets and benchmarks to measure success where no single correct answer exists.
Required Qualifications
- 3+ years of applied AI / ML, with systems you've taken to production (not just prototypes)
- Masters degree in STEM
- Location: New York, NY (Relocation supported)
Desired Qualifications
- Depth in at least one, and working fluency across several, of: time-series / forecasting and statistical modeling; LLM and agentic systems; large-scale messy-data engineering
- You build evaluation and monitoring as a matter of course — and know which metric to trust, and how to avoid leakage and train/serve skew
- Strong product sense: you turn AI capability into real outcomes, and know when a simpler approach wins
- Demand forecasting / time-series (Prophet, ARIMA-family, gradient-boosted)
- Agentic workflows, RAG, or retrieval systems in production
- Document understanding / information extraction from unstructured sources
- Startup experience or comfort in fast-moving environments
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