Senior Machine Learning Engineer

Alt is unlocking the value of alternative assets through a platform for buying, selling, vaulting, and financing trading cards. The Senior Machine Learning Engineer will own the full production lifecycle of pricing and underwriting models, improving coverage, accuracy, cost, freshness, and risk performance while operating the AWS infrastructure and serving systems behind them.

Responsibilities:

• Leaner, sharper pricing. Cut infrastructure cost meaningfully while improving accuracy, especially on high-value assets where being wrong is expensive
• Underwriting from good to great. Iterate the model to maximize cash advance disbursements without breaching risk thresholds or default targets
• The full ML lifecycle. Feature generation and training through deployment and monitoring. No handoff, no throwing it over a wall
• The production path. The models' AWS infrastructure and the pricing APIs themselves — capacity planning, autoscaling, latency, and diagnosing the memory and timeout failures that only show up at scale
• Experiments that settle arguments. Design and run backtests to find and validate features that actually move predictive power and coverage
• Domain depth. Work directly with our Expert Pricers until your model changes reflect real market judgment, not just what the data allows
• Build an agent that reads a batch of new comps and flags the ones our model is likely to price badly, before a pricer has to catch it by hand
• Use foundation models for feature extraction from unstructured card and listing data — condition language, provenance notes, auction descriptions — and get real lift out of it
• Stand up a backtest harness you can talk to, so evaluating a feature idea is a conversation rather than a two-day branch
• Put an MCP server over the model registry and prediction logs so "why did this card price move" is a question, not a query
• Use Claude Code or Cursor as the default way you work through a refactor or an infra migration, not as autocomplete

Requirements:

• 7+ years engineering, with 5+ years building and shipping production ML/AI models
• Production-grade Python and SQL, including custom feature-engineering pipelines — not just off-the-shelf scikit-learn. Time-decay weighting, leakage-safe k-fold cross-validation, cascading fallback and imputation logic
• Gradient-boosted or ensemble estimators trained and validated against strict accuracy tolerances, with segment-specific tuning by category or asset type
• LLMs and foundation models in production, for both internal tooling and user-facing product
• MLflow or a comparable tool for experiment tracking and model registry/versioning
• Production model-serving on AWS that was yours — capacity planning, autoscaling, and diagnosing memory and timeout failures at scale
• CI/CD, production workflow orchestration, and IaC
• A pragmatic bias. Value delivered incrementally over the perfect rebuild
• Bonus: real-time or low-latency serving at scale
• Startup experience
• You collect, or you have opinions about the collectibles market