Senior AI/ML Engineer 

April 10, 2026
Application ends: July 9, 2026

Job Description

REQUIREMENTS

  • 5+ years of Python experience in production ML systems (beyond notebooks/research).
  • Deep experience with statistical modeling, including ensemble methods, kNN, calibration, cross-validation, and feature engineering.
  • Expertise in time-series modeling & forecasting, including seasonality, trend decomposition, safety stock, and demand planning.
  • Proven track record of shipping ML models that drive real business decisions (forecasting, pricing, demand planning).
  • Strong intuition for messy, real-world data, including bias correction, stale signal handling, error cancellation, and distribution shifts.
  • Experience with API integration and data pipeline architecture at scale.
  • Hands-on experience with LLM/AI-agent workflows, including prompt engineering and evaluation frameworks.
  • Proven ability to validate models rigorously: LOO, backtesting, production vs offline metric gaps.
  • Self-directed, comfortable in a fast-evolving, small team environment.

Preferred

  • Experience in Amazon marketplace, e-commerce, or retail analytics.
  • Familiarity with similarity-based methods (kNN, embeddings, vector search).
  • Experience maintaining long-lived model systems (v1 → v30+ iteration cycles).
  • Prior startup or founder-adjacent experience.

RESPONSIBILITIES

  • Build and improve inventory demand forecasting models using ML and statistical methods.
  • Own ML models end-to-end: data collection → feature engineering → training → deployment → monitoring → iteration.
  • Develop decision systems that support inventory planning, pricing, and demand decisions.
  • Build and maintain data pipelines and API integrations for external and internal data sources.
  • Work with messy real-world data to ensure model reliability through rigorous validation and testing.
  • Implement LLM/AI-agent workflows to translate domain logic into automated processes.
  • Operate independently in a small team, setting priorities, unblocking challenges, and communicating tradeoffs clearly.

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