Staff ML Engineer - Recommender Systems (Remote)

United States Digital Space LLC; United States; Remote; USD 272,000 - 340,000; Full time

Job summary

United States Digital Space LLC is seeking a Staff Machine Learning Engineer for the Consumer Revenue ML team. You will own applied ML across core revenue surfaces, building ranking, targeting, and recommendation systems to connect users with products and content.

You’ll lead org-wide ML initiatives, ship models from 0→1 to production at scale, and collaborate across product and engineering teams to translate experiments into roadmaps. This role can be remote in the US with SF office optional.

Qualifications

• 8+ years of applied ML experience, including graduate degree.
• Strong expertise in deep learning and RecSys architectures.
• Proficiency in Python and ML frameworks (PyTorch/JAX/TF).
• Experience shipping ML systems from 0→1 to production at scale.
• Ability to translate experiment results into product roadmap decisions.
• Excellent cross-functional collaboration and communication.

Responsibilities

• Lead ML initiatives across Shop, Nitro, Server Subscriptions, and Gifting.
• Architect and implement ranking, targeting, and recommendation systems.
• Collaborate with product, data, and engineering teams to align stakeholders.
• Drive production-ready ML pipelines and platform tooling.
• Translate experiments into actionable business decisions.

Skills

Applied ML

Python

Deep learning

Two-tower models

Transformer models

Production at scale

Cross-functional leadership

Business intuition

Experimentation to roadmap

Communication

Education

PhD or Master's in CS/ML

Machine Learning or AI related field

Tools

PyTorch

JAX

TensorFlow

Ray

Anyscale

Chronon

Spark

Flink

Job description

United States Digital Space LLC is seeking a Staff Machine Learning Engineer for the Consumer Revenue ML team. You will own applied ML across core revenue surfaces, building ranking, targeting, and recommendation systems to connect users with products and content.

You’ll lead org-wide ML initiatives, ship models from 0→1 to production at scale, and collaborate across product and engineering teams to translate experiments into roadmaps. This role can be remote in the US with SF office optional.