Staff Machine Learning Engineer

Tilt builds mobile-first financial products and machine-learning-powered credit models. The Staff Machine Learning Engineer, ML Platform owns the platform between data science and infrastructure, operating the managed feature store and building training and deployment platforms that help data scientists work independently.

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

Requirements:

• 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