Staff Machine Learning Engineer

About the role

JOIN THE TILT TEAM

At Tilt, we see a side of people that traditional lenders miss. Our mobile-first products and machine learning-powered credit models look beyond credit scores, using over 250 real-time financial signals to recognize real potential. With millions of customers worldwide, we're not just changing how people access financial products — we're creating a new credit system that backs the working, whatever they're working toward.

THE OPPORTUNITY: STAFF MACHINE LEARNING ENGINEER, ML PLATFORM

ML is core to what Tilt does: our models make credit decisions for millions of customers across multiple markets, and they run on a platform shared by several business units. You’ll own the layer between data science and infrastructure. Half your time goes to running our managed feature store, and the other half to building the training and deployment platforms that let data scientists ship without an engineer in the loop.

The foundation is in place: a feature store, standard pipelines and an automated deployment framework. The team is now moving quickly into deep learning and streaming models. You’ll own the platform layer that sits between data science and infrastructure, and your measure of success is how much faster and more independently data scientists can move because you built it. You’ll thrive here if you go find the pain points yourself, turn them into platform work, and stay with each project until data scientists are actually using it.

Tilt is remote-first. We come together in person at least twice a year for company offsites — and those gatherings are worth the trip.

HOW YOU’LL MAKE AN IMPACT

- Run our managed feature store: build the patterns and tooling for defining, backfilling, and serving features so a new model can be trained from existing features and deployed.

- Make the feature store the starting point for model training, not just deployment, so new models are trained on existing features instead of rebuilt pipelines.

- Build and improve our automated training and deployment platforms. Our ML engineers own frameworks, not individual models.

- Turn the steps data scientists repeat (registering features, launching training jobs, promoting a model, setting up monitoring) into self-service agents and skills they run themselves.

- Standardize training and retraining so refits are schedulable, reproducible and backed by feature store data rather than one-off queries.

- Scale batch scoring and low-latency online inference, with autoscaling, resource isolation and cost controls that hold up as the number of models grows.

- Extend monitoring for data drift, feature drift, prediction quality and serving health, with alerts and dashboards data scientists use without escalating to engineering.

- Define standards for model and feature versioning, lineage, testing and documentation, and lead their adoption across data science teams.

WHY YOU’RE A GREAT FIT