Senior Staff ML Engineer - Discovery & Recs (Hybrid)

Grubhub Holdings Inc.; United States; Hybrid; USD 240,000 - 250,000; Full time

Benefits offered by this job

Equity

401(k)

Medical, dental, and vision plans

Paid time off

Parental leave

Discounted meals

Job summary

Grubhub, part of Wonder, is seeking a Senior Staff Machine Learning Engineer to lead the ML engine behind Discovery—ranking, recommendations, and retrieval that shape what diners see in the app. This hands-on role owns model architecture across ranking, homepage suggestions, and query understanding while mentoring engineers and shaping roadmaps.

Based in New York with a hybrid work model (3 days in the office), the role offers substantial impact on diner experience, merchant reach, and growth

Qualifications

• MS/PhD in a quantitative discipline (Computer Science, Math, Physics, Engineering, Statistics or other technical field) or equivalent experience.
• 8+ years building and shipping machine learning systems, including 3+ years operating at staff-level scope across multiple teams, model families, or systems.
• Deep experience in recommendation systems, ranking, or information retrieval at scale, with real latency and cost constraints.
• Proven track record with production deep learning in TensorFlow or PyTorch, including training, serving, and tuning runtime models on GPUs.
• Experience with Large Language Models and transformer-based architectures, including fine-tuning, embedding generation, and deployment in latency-sensitive apps.
• Experience with language understanding over imperfect grammar (real-world search queries, catalog text) is a strong plus.
• Strong data engineering fundamentals: PySpark, Hive/SQL, the Python data stack, and feature pipelines you can debug and build.
• Fluency with experiment design: A/B tests, guardrails, offline metrics interpretation.
• Experience with cloud ML infrastructure (AWS/SageMaker or equivalents), model deployment, and production monitoring.
• Demonstrated technical leadership: mentoring engineers, driving design/reviews beyond your team, influencing decisions without direct authority.
• Comfort communicating performance metrics and trade-offs to technical and non-technical stakeholders, including executives.
• Ability to stay current with research and synthesize it into production systems.
• Deep interest in continuous learning and self-motivated contribution.
• Our hybrid model requires 3 days a week in the office; in NY you may come in more for collaboration.

Responsibilities

• Own the architecture of the ranking and recommendation stack end-to-end: candidate retrieval, multi-objective ranking, calibration, and ensemble design.
• Lead cross-system design decisions and drive the evolution of optimization objectives toward long-term diner value.
• Advance state-of-the-art research in information retrieval and recommender systems into production, including LLM-driven query understanding and embedding-based retrieval.
• Mentor senior and mid-level engineers, participate in hiring, and raise technical depth across the team.
• Translate technical trade-offs into business terms for product and executive stakeholders, documenting rationale clearly.

Skills

Machine learning systems

Staff-level technical leadership

Recommendation systems

Information retrieval

Experimentation design

Cross-team communication

Cloud ML infrastructure

Mentoring engineers

Research synthesis

Data-driven decision making

Biased towards impact

Hybrid collaboration

Education

MS/PhD in Computer Science, Math, Engineering, or related field

Tools

TensorFlow

PyTorch

AWS SageMaker

PySpark

Hive/SQL

Python data stack

Job description

Grubhub, part of Wonder, is seeking a Senior Staff Machine Learning Engineer to lead the ML engine behind Discovery—ranking, recommendations, and retrieval that shape what diners see in the app. This hands-on role owns model architecture across ranking, homepage suggestions, and query understanding while mentoring engineers and shaping roadmaps.

Based in New York with a hybrid work model (3 days in the office), the role offers substantial impact on diner experience, merchant reach, and growth