Staff Machine Learning Engineer

TiltCredit machine learning

Remote - USFull-TimeStaff

Salary$220K - $265K; $220K – $265K • Offers Equity • Offers Bonus

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Job Details

Bring hands-on platform or software engineering experience working in machine learning.
Have a track record of platforms serving many production models across multiple teams.
Have experience with feature stores and model deployment platforms, such as Databricks Feature Store, Feast, or Tecton.
Have strong ML CI/CD experience with tools such as GitHub Actions, Jenkins, or Azure Pipelines.
Know model registries and infrastructure as code with Terraform.
Bring AWS or Azure cloud expertise for ML deployment.
Have experience with Kubernetes, workflow orchestration, and batch and real-time serving.
Understand autoscaling and latency and cost trade-offs.
Have built self-service tooling adopted by data scientists to reduce engineering involvement in deployment.
Have worked with backend engineers to put deployments into production.
Be able to gather stakeholder requirements, propose solutions, and drive projects to completion.
Have sufficient knowledge of model development and common algorithms to partner technically with data scientists.

Run the managed feature store and build tooling for defining, backfilling, and serving features.
Make existing feature-store data the starting point for model training and deployment.
Build and improve automated model training and deployment platforms.
Turn recurring data-science workflows into self-service agents and tools.
Standardize schedulable, reproducible training and retraining using feature-store data.
Scale batch scoring and low-latency online inference with autoscaling, resource isolation, and cost controls.
Extend monitoring for data drift, feature drift, prediction quality, and serving health.
Define and lead adoption of standards for model and feature versioning, lineage, testing, and documentation.

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