Staff Machine Learning Engineer

Wonder•New York, NY

1d•$208,500 - $225,000•Hybrid

About The Position

Grubhub is seeking a Staff Machine Learning Engineer to provide hands-on technical leadership for the Discovery machine learning systems that power ranking, recommendations, and retrieval. These systems determine what diners see when they open the app or search, and support a large catalog of merchants and menu items. The role owns model architecture across search ranking, homepage and topic recommendations, retrieval, and query understanding, with responsibility for how these systems work together. It is a technical leadership role, not a management role. The engineer will set technical direction with staff engineers, product managers, and platform partners; raise standards for building, evaluating, and operating models; and mentor engineers through reviews and feedback. The team owns projects end to end and works on personalized recommendation, retrieval, and classification using catalog content and clickstream data. Relevant approaches include deep neural networks, learned embeddings, approximate nearest-neighbor retrieval, transfer learning, calibration, regression, fine-tuning, and large language models. The position is based on a hybrid model requiring three office days per week, with the option and encouragement to work in the office up to five days per week. The listed New York salary range is $208,500–$225,000 per year; offers may vary by geography and candidate qualifications. The role's benefits include equity, a 401(k), medical, dental, and vision plans, company-paid short- and long-term disability coverage, paid time off, paid parental leave, discounted meals, and perks across the Wonder family of brands. Eligibility and plan options vary by employment classification and location.

Requirements

Master's or Ph.D. in a quantitative discipline such as computer science, mathematics, physics, engineering, or statistics, or equivalent experience.
At least 6 years building and shipping machine learning systems, including at least 3 years operating at senior-level scope and setting technical direction across multiple teams, model families, or systems.
Deep production experience with recommendation systems, ranking, or information retrieval at scale, under real latency and cost constraints.
Production deep-learning experience with TensorFlow or PyTorch, including model training, serving, and runtime tuning on GPUs.
Experience with large language models and transformer-based architectures, including fine-tuning, embedding generation, and deployment in latency-sensitive applications.
Strong data engineering fundamentals, including PySpark, Hive/SQL, the Python data stack, and the ability to build and debug feature pipelines.
Experience designing A/B tests, selecting guardrails, and assessing when offline metrics may be misleading.
Experience with cloud ML infrastructure such as AWS/SageMaker or equivalent, model deployment, production monitoring, and observability.
Demonstrated technical leadership through mentoring, design and architecture reviews beyond one's own team, and influencing decisions without direct authority.
Ability to communicate performance metrics, model behavior, and technical trade-offs to technical and non-technical audiences, including executives.
Ability to follow current research publications and synthesize them into production systems.
Strong interest in self-motivated continuous learning.

Nice To Haves

Experience with language understanding for imperfect grammar in real-world search queries and menu or catalog text.

Responsibilities

Own the end-to-end architecture of the ranking and recommendation stack, including candidate retrieval, multi-objective ranking, calibration, and the ensemble balancing conversion and profitability.
Lead the evolution of optimization objectives toward long-term diner value, including offline evaluation and online experimentation.
Evaluate and integrate relevant advances in information retrieval and recommender systems into production, including LLM-driven query and intent understanding, embedding-based retrieval, sequential user representations for cold start, and real-time inference.
Improve engineering and operational standards across teams, including model scorecards and evaluation, reproducible training pipelines, safe deployment, SLOs, observability for tier-one models, and proactive technical-debt management.
Partner with Product, Search Engineering, Ads, and Data Platform to shape roadmaps, identify risks, and ensure required data and infrastructure are available.
Mentor senior and mid-level engineers, participate in hiring, and build team technical depth to reduce dependence on individual people for critical systems.
Explain technical trade-offs in business terms to product and executive stakeholders and document decision rationales.
Challenge assumptions, identify innovation opportunities, and analyze and improve business performance.