Staff Machine Learning Engineer

Tilt Finance

1d • $220,000 - $265,000 • Remote

About The Position

Tilt is hiring a Staff Machine Learning Engineer for its ML Platform. The role owns the platform layer between data science and infrastructure, helping data scientists train, deploy, and operate machine-learning models independently. The work is split between operating Tilt’s managed feature store and building training and deployment platforms. The platform supports models that make credit decisions for millions of customers across multiple markets and business units.

The foundation includes a feature store, standard pipelines, and an automated deployment framework. The team is expanding into deep learning and streaming models. Success is measured by how much faster and more independently data scientists can work through the platform. The role calls for identifying pain points, turning them into platform improvements, and following projects through to adoption by data scientists.

Tilt is remote-first, with in-person company offsites at least twice a year. The position works closely with data scientists and backend engineers and involves setting platform standards across data science teams.

Requirements

• Deep hands-on experience as a platform or software engineer working in machine learning.
• A track record of building platforms that served many production models across multiple teams.
• Experience with feature stores and model deployment platforms, such as Databricks Feature Store, Feast, Tecton, or equivalent.
• Strong CI/CD experience for machine learning, model registries, and infrastructure as code with Terraform.
• Cloud expertise in AWS or Azure for ML deployment.
• Experience with Kubernetes and workflow orchestration.
• Strong command of batch and real-time serving, autoscaling, and latency and cost trade-offs.
• Experience creating self-service tooling that data scientists adopted and that reduced engineers’ involvement in deployment.
• Experience working alongside backend engineers to bring deployments live.
• Ability to gather stakeholder requirements independently, propose solutions, and drive projects to completion amid ambiguity.
• Sufficient depth in model development and common algorithms to act as a technical partner to data scientists.

Nice To Haves

• Experience with a managed feature store.
• Familiarity with PySpark, Databricks, and monitoring tools such as Evidently AI.

Responsibilities

• Run the managed feature store and build patterns and tooling for defining, backfilling, and serving features.
• Make the feature store the starting point for model training so models use existing features rather than rebuilt pipelines.
• Build and improve automated model training and deployment platforms and frameworks.
• Turn recurring data-science tasks—such as registering features, launching training jobs, promoting models, and setting up monitoring—into self-service agents and skills.
• Standardize training and retraining so refits are schedulable, reproducible, and backed by feature-store data.
• Scale batch scoring and low-latency online inference, including autoscaling, resource isolation, and cost controls.
• Extend monitoring for data drift, feature drift, prediction quality, and serving health, with useful alerts and dashboards.
• Define standards for model and feature versioning, lineage, testing, and documentation, and lead their adoption across data science teams.
• Identify platform pain points, propose solutions, and drive projects through to completion and adoption.
• Work alongside backend engineers to get model deployments live.

Benefits

• Work-from-home office reimbursement.
• Competitive compensation and generous equity.
• Flexible health plans at every premium level, with substantial subsidies.
• Direct exposure to the leadership team.
• Paid global company onsites twice yearly.
• Growth opportunities based on contributions rather than rigid promotion timelines.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed