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.
🏢 About Tilt
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 Role
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.
✅ Key Responsibilities
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.
📌 Required Qualifications
Deep hands-on experience as a platform or software engineer working in ML, with a track record of platforms that served many models in production across multiple teams.
Experience with feature stores and model deployment platforms, such as Databricks Feature Store, Feast, Tecton or equivalent. Time on a managed feature store is especially relevant.
Strong CI/CD for ML (GitHub Actions, Jenkins, Azure Pipelines or similar), model registries, and infrastructure as code with Terraform.
Cloud expertise in AWS or Azure for ML deployment, plus Kubernetes, workflow orchestration, and a strong command of batch and real-time serving, autoscaling, and latency and cost trade-offs.
You’ve removed engineers from the deployment loop with self-service tooling that data scientists actually adopted, and you’ve worked side by side with backend engineers to get deployments live instead of handing models off.
You work well with ambiguity: you gather requirements from stakeholders without being directed, propose solutions and drive projects to completion.
Enough depth in model development and common algorithms to be a real technical partner to data scientists. Familiarity with PySpark, Databricks and monitoring tools such as Evidently AI is a plus.
🎁 Benefits
Virtual-first teamwork: The Tilt team is collaborating across 14 countries, 12 time zones, and counting. You’ll get started with a WFH office reimbursement.
Competitive pay: We're big on potential, and it's reflected in our competitive compensation packages and generous equity.
Complete support: Find flexible health plans at every premium level, and substantial subsidies that stand up to global standards.
Visibility is yours: You can count on direct exposure to our leadership team — we’re a team where good ideas travel quickly.
Paid global onsites: Magic happens IRL: we gather twice yearly to reconnect over shared meals or kayaking adventures. (We’ve visited Vail, San Diego, and Mexico City, to name a few.)
Impact is recognized: Growth opportunities follow your contributions, not rigid promotion timelines.
🛂 Visa & Eligibility
We're looking for people who chase excellence and impact. Those who stand behind their work, celebrating the wins and learning from the missteps equally. We foster
