Senior Staff ML Engineer — Recommender Systems Lead

GrubHub Inc.; New York (NY); Hybrid; USD 240,000 - 250,000; Full time

Benefits offered by this job

Equity

401(k)

Medical/Dental/Vision

Disability coverage

Paid time off

Job summary

Grubhub is seeking a Senior Staff Machine Learning Engineer to lead the end-to-end machine learning stack for Discovery, including ranking, recommendations, and retrieval. You will own model architecture across multiple connected systems and mentor engineers while partnering with product and data platform teams.

The role demands 8+ years in ML with staff-level impact, deep experience in recommender systems, and proficiency with TensorFlow/PyTorch, AWS/SageMaker, and large language models.

Qualifications

• 8+ years building and shipping ML systems, with staff-level scope.
• Deep experience in ranking, retrieval, and recommender systems at scale.
• Production DL experience with TensorFlow or PyTorch.

Responsibilities

• Own architecture of ranking and recommendation stack end to end.
• Lead multi-system design decisions across teams and modules.
• Mentor engineers and participate in design and architecture reviews.
• Translate technical trade-offs into business terms for stakeholders.
• Drive adoption of robust evaluation, instrumentation, and deployable ML systems.

Skills

ML systems engineering

Recommendation systems

Deep learning (TensorFlow/PyTorch)

Large Language Models

Data engineering

A/B testing

Cloud ML infra (AWS/SageMaker)

Mentorship/leadership

Education

MS/PhD in CS/Applied Math/Engineering/Statistics

Tools

TensorFlow

PyTorch

SageMaker

SQL

PySpark

Hive

Job description

Grubhub is seeking a Senior Staff Machine Learning Engineer to lead the end-to-end machine learning stack for Discovery, including ranking, recommendations, and retrieval. You will own model architecture across multiple connected systems and mentor engineers while partnering with product and data platform teams.

The role demands 8+ years in ML with staff-level impact, deep experience in recommender systems, and proficiency with TensorFlow/PyTorch, AWS/SageMaker, and large language models.