Staff Machine Learning Engineer

Tilt (formerly Empower)

. Run the managed feature store, including tooling for defining, backfilling and serving features .

Posted 11/5/2026full-timeRemote • United StatesLead💰 $220,000 - $265,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in managing feature stores and automating ML training and deployment processes, with a strong focus on collaboration with data scientists and backend engineers. Proficient in cloud deployment, CI/CD practices, and monitoring for model performance and data integrity.

Highest-signal resume keywords

Feature Store ManagementCI/CD For MLCloud Expertise In AWS Or AzureKubernetes And Workflow OrchestrationModel Deployment Platforms

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel DeploymentFeature EngineeringBatch ServingReal-Time ServingModel VersioningInfrastructure As CodeData Drift MonitoringModel RegistriesCommon Algorithms

Soft Skills

Project ManagementCollaborationProblem Solving

Tools & Technologies

Databricks Feature StoreFeastTectonGitHub ActionsJenkinsAzure PipelinesTerraformKubernetesPySparkEvidently AI

Industry Keywords

Machine Learning PlatformsModel TrainingFeature RegistrationMonitoringResource Isolation

Tech Stack

Tools & technologies

AWSAzureCloudJenkinsKubernetesPySparkTerraform

About the role

Key responsibilities & impact

Run the managed feature store, including tooling for defining, backfilling and serving features
Make the feature store the starting point for model training and deployment
Build and improve automated ML training and deployment platforms
Create self-service agents and skills for feature registration, training jobs, model promotion and monitoring
Standardize schedulable, reproducible training and retraining backed by 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
Partner with data scientists and backend engineers to improve platform adoption and deployment speed

Requirements

What you’ll need

Deep hands-on experience as a platform or software engineer working in ML
Track record of platforms serving many models in production across multiple teams
Experience with feature stores and model deployment platforms such as Databricks Feature Store, Feast, Tecton or equivalent
Strong CI/CD for ML, including GitHub Actions, Jenkins, Azure Pipelines or similar
Experience with 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, latency and cost trade-offs
Experience building self-service tooling adopted by data scientists
Experience collaborating with backend engineers to deploy models
Ability to gather requirements, propose solutions and drive projects to completion amid ambiguity
Sufficient depth in model development and common algorithms to partner technically with data scientists
Familiarity with PySpark, Databricks and Evidently AI is a plus

Benefits

Comp & perks

Equity
Bonus
WFH office reimbursement
Flexible health plans at every premium level
Substantial health-plan subsidies
Paid global onsites twice yearly
Growth opportunities based on contributions
Virtual-first teamwork