Staff Machine Learning Engineer (ML Platform)

Quick Facts

• Role: Staff Machine Learning Engineer (ML Platform)

• Focus: ML platform bridging data science and infrastructure; managed feature store; automated training and deployment

Description

You will own the platform layer that helps data scientists train and deploy models faster with less engineering involvement. About half your time is dedicated to running and improving a managed feature store, while the other half is focused on building training and deployment platform capabilities. You’ll work on frameworks owned by ML engineers (not individual models) as the team moves into deep learning and streaming models.

Responsibilities

• Run a managed feature store: define patterns and tooling for feature definition, backfilling, and serving

• Make the feature store the starting point for model training (not only deployment)

• Build and improve automated training and deployment platforms

• Turn repetitive data-science steps into self-service agents and skills

• Standardize training and retraining for schedulable, reproducible refits 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 with alerts and dashboards

• Define standards for model and feature versioning, lineage, testing, and documentation

Requirements

• Deep hands-on experience as a platform or software engineer working in ML, including production platforms serving many models across multiple teams

• Experience with feature stores and model deployment platforms such as Databricks Feature Store, Feast, or Tecton (managed feature store experience especially relevant)

• Strong CI/CD for ML, model registries, and infrastructure as code with Terraform

• Cloud expertise deploying ML on AWS or Azure, plus Kubernetes, workflow orchestration, and batch + real-time serving with autoscaling and latency/cost trade-offs

• Built self-service tooling that data scientists actually adopted

• Collaborated with backend engineers to get deployments live

• Comfort working with ambiguity: gather requirements from stakeholders and drive projects to completion

• Technical depth in common ML algorithms; familiarity with PySpark, Databricks, and Evidently AI is a plus

Benefits

• Virtual-first teamwork across multiple countries and time zones, with WFH office reimbursement

• Competitive pay with generous equity

• Flexible health plans at multiple premium levels with substantial subsidies

• Direct exposure to leadership

• Paid global on-sites (twice yearly)

• Growth opportunities recognized based on contributions