Senior Machine Learning Engineer

Job Description

We're hiring a Senior Machine Learning Engineer who thrives on owning models end-to-end, from research through production. In this role, you'll own productization of Alt's pricing and underwriting models — the systems that turn raw card and market data into the Alt Value and cash advance terms that every buyer, seller, and lender on the platform depends on. You'll be the person who matures models to production-grade services, keeps them accurate and fast at scale, and pushes the boundary of what we can automate.

Why This Role Exists

Alt is at an inflection point — our marketplace is scaling fast, and our pricing intelligence infrastructure has become a genuine competitive moat. We've proven that model-driven pricing works; now we need to push coverage, accuracy, and speed further while bringing down the overhead to run it. This is a high-ownership opportunity to take our pricing and underwriting models from "working" to "excellent" — and to define the ML infrastructure standards that will scale with the company for years to come.

What You'll Own

• Optimize our pricing models to significantly reduce infrastructure costs while maintaining and improving their accuracy, especially for high-value assets.
• Iterate on our underwriting model to maximize cash advance disbursements while maintaining target risk thresholds and default rates.
• Lead the full ML lifecycle from model training and feature generation to production deployment and monitoring.
• Collaborate closely with our Expert Pricers to become a domain expert in the trading card market and inform model improvements.
• Design and execute experiments and backtesting to discover and validate new features that improve the models’ predictive power and coverage.
• Own the models’ AWS infrastructure, writing code for our pricing APIs to ensure the models can serve at scale and with low latency.

Metrics You’ll Own:

Northstar Metric: Model-Based Pricing Coverage (% of cards confidently priced by models vs. manually)

KPIs:

• Pricing Accuracy (% Error)

• Pricing Freshness (End-to-End Model Orchestration Time)

• Underwriting Performance (Advance disbursement rate vs. Target default rate)

What Great Looks Like (6 Months)

• Shipped leaner, more accurate pricing models. You've cut infrastructure cost meaningfully while improving accuracy, especially on high-value assets.

• Moved underwriting from good to great. You've iterated on the underwriting model to increase cash advance disbursements without breaching risk thresholds.

• Earned trust with Expert Pricers. You're a go-to partner for the pricing team — you understand the domain deeply enough that your model changes reflect real market judgment, not just data.

• Hardened the production path. The pricing APIs are faster, more observable, and easier to reason about, with monitoring in place to catch model drift or degradation before it hits customers.

Who you are

Must-haves:

• 7+ years of engineering experience, with 5+ years building and shipping production ML/AI models.
• Deep proficiency in production-grade Python and SQL, including building custom feature-engineering pipelines (not just off-the-shelf scikit-learn). Think time-decay weighting, leakage-safe k-fold cross-validation, and cascading fallback/imputation logic.
• Experience training and validating gradient-boosted or ensemble estimators against strict accuracy/error tolerances, with segment-specific tuning (e.g., by category or asset type).
• Experience leveraging LLMs, foundation models, and AI dev tools for both internal tooling and user-facing product use cases in production.
• Experience with MLflow or a comparable tool for experiment tracking and model registry/versioning.
• Comfortable owning production model-serving infrastructure on AWS — capacity planning, auto-scaling, and diagnosing memory/timeout failures at scale.
• Experience with CI/CD pipelines, orchestrating production workflows, and IaC for provisioning and modifying cloud infrastructure.
• Pragmatic and focused on delivering value incrementally rather than pursuing perfection.

Nice-to-haves:

• Experience with real-time or low-latency models serving at scale.
• Previous startup experience — you understand and thrive on the pace, adaptability, and ownership required in a fast-moving environment.
• Interested in or knowledgeable of trading cards, collectibles, or alternative asset markets.

What You'll Get From Us

• A seat at the table to help shape the future of Alt and the alternative asset space
• Autonomy and ownership on projects that matter
• $100/month work-from-home stipend
• $200/month wellness stipend
• WeWork office stipend
• 401(k) retirement benefits
• Flexible vacation policy
• Generous paid parental leave
• Competitive healthcare benefits, including HSA, for you and your dependent(s)

Base salary range: $235,000-250,000 plus equity. Offers may vary based on experience, location, and other factors.