Staff ML Platform Engineer — Remote & Scalable ML

Tilt; United States; Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

Virtual-first teamwork and WFH support

Competitive pay and generous equity

Comprehensive health plans

Direct exposure to leadership

Global onsite visits

Equity

Job summary

Tilt is hiring a Staff Machine Learning Engineer to own the ML Platform layer, connecting data science with infrastructure. You’ll manage our feature store and build training/deployment platforms that let data scientists ship models without heavy engineering handoffs.

The role emphasizes self-service tooling, scalable training, and end-to-end ML workflows. Tilt is remote-first, with in-person offsites a couple of times per year, and extensive equity opportunities.

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 (Databricks, Feast, Tecton or equivalents).
• Strong CI/CD for ML and infrastructure as code (Terraform).
• Cloud expertise in AWS or Azure for ML deployment, plus Kubernetes and latency/cost trade-offs.
• Ability to remove engineers from the deployment loop with self-service tooling adopted by data scientists.
• Comfort working in ambiguity and driving projects to completion.
• Knowledge of PySpark, Databricks, and monitoring tools such as Evidently AI is a plus.

Responsibilities

• Run our managed feature store: define, backfill, and serve features for training and deployment.
• Make the feature store the starting point for model training, not just deployment.
• Build and improve automated training and deployment platforms.
• Turn routine data-science steps into self-service agents and skills.
• Standardize training/retraining to be schedulable, reproducible, and data-store-backed.
• Scale batch scoring and low-latency online inference with autoscaling and cost controls.
• Extend monitoring for data drift, feature drift, and model health with actionable dashboards.
• Define standards for model/feature versioning and lineage and drive adoption across teams.

Skills

ML Platform Engineering

Feature Store Management

Model Training & Deployment

CI/CD for ML

Cloud (AWS/Azure)

Kubernetes

Self-Service Tooling

Tools

Databricks Feature Store

Feast

Tecton

Terraform

Job description

Tilt is hiring a Staff Machine Learning Engineer to own the ML Platform layer, connecting data science with infrastructure. You’ll manage our feature store and build training/deployment platforms that let data scientists ship models without heavy engineering handoffs.

The role emphasizes self-service tooling, scalable training, and end-to-end ML workflows. Tilt is remote-first, with in-person offsites a couple of times per year, and extensive equity opportunities.