Lead Quantitative Research Engineer

Alt is unlocking the value of alternative assets, starting with the $5 B trading-card market. We let collectors buy, sell, vault, and finance their cards in one place and we are backed by leaders at Stripe, Coinbase, Seven Seven Six, and pro athletes like Tom Brady and Giannis Antetokounmpo. Our next frontier is real-time pricing at scale—the Alt Value that powers every trade, loan, and product on the platform.

The Opportunity

You will own the pricing engine that sets Alt Value across millions of cards and future asset classes. Think of it as building Bloomberg Fair-Value for collectibles. You will architect the models, production pipeline, and monitoring that turn messy auction data into live data our customers trust.

What You’ll Tackle

• Build the price engine. Design, train, and deploy time-series and tree-based models (XGBoost, CatBoost, sklearn, lightGBM) that predict fair value and forecast volatility.
• Harden the data layer. Ingest and reconcile auction feeds, marketplace listings, and private-sale data. Handle splits, dupes, zero-comp situations, and stale marks.
• Ship to production. Own model orchestration with Airflow, feature stores, real-time inference endpoints, and rollback strategies.
• Quantitative RD. Test market microstructure effects (extended bidding, buyer premiums, cash advances) and bake insights into pricing logic.
• API analytics. Expose Alt Value as a public API, power in-app price alerts, and deliver dashboards the business can act on.
• Lead the function. Hire and mentor a small team of DS/MLEs and set the standard for code quality, review, and testing.

Tech Stack

Python, SQL, AWS (S3, ECS, Lambda), Airflow, dbt, Postgres, Spark, XGBoost, sklearn, CatBoost, GitHub Actions. (Nice-to-have: TypeScript, FastAPI, Grafana, Datadog)

What You Bring

• 9+ years in quantitative finance, ML engineering, or similar.
• Deep time-series and forecasting experience, ideally on illiquid or auction assets.
• Proven path from Jupyter to production with CI/CD, testing, and automated monitoring.
• Track record of improving MAE or PnL with your models in live systems.
• Fluent in Python, SQL, and modern data tooling.
• Strong communication: you can explain heteroscedastic noise to engineers

Compensation Perks

• Founder-level impact on a green-field pricing platform
• $200 / month remote-work stipend, $100 / month wellness stipend, and WeWork access
• Flexible PTO and generous parental leave
• Premium health, dental, vision, and HSA
• 401(k)
• Base salary: $275K – $350K, plus equity. Offers may vary based on experience, location, and other factors.

Ready to Set the Market?

If you can price the unpriceable and want to define the reference rate for alternative assets, we would love to meet you. Apply with a short note on the most difficult model you have put into production and why it mattered.

Originally posted on Himalayas