Senior Staff Machine Learning Engineer

Wonder•New York, NY

1d•$240,000 - $249,500•Hybrid

About The Position

Grubhub is seeking a Senior Staff Machine Learning Engineer for a hands-on technical leadership role on its Discovery team. The role leads the machine learning systems behind search ranking, homepage and topic recommendations, retrieval, and query understanding—systems that influence what diners see across the marketplace. The engineer owns architecture across these connected areas and makes cross-system design decisions, rather than managing people.

Requirements

MS or PhD in a quantitative discipline such as computer science, mathematics, physics, engineering, or statistics, or equivalent experience.
At least 8 years building and shipping machine learning systems, including at least 3 years operating at staff-level scope by setting technical direction across multiple teams, model families, or systems.
Deep production experience in recommendation systems, ranking, or information retrieval at scale and under real latency and cost constraints.
Production deep-learning experience with TensorFlow or PyTorch, including training, serving, and tuning runtime models on GPUs.
Experience with large language models and transformer 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 building and debugging feature pipelines.
Experience designing A/B tests, selecting guardrails, and identifying when offline metrics are misleading.
Experience with cloud ML infrastructure such as AWS/SageMaker or equivalent, model deployment, and production monitoring and observability.
Demonstrated technical leadership, including mentoring engineers, driving design and architecture reviews beyond one's own team, and influencing 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 over imperfect grammar, such as 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 shift in optimization objectives from short-term conversion toward long-term diner value, including offline evaluation and online experimentation.
Evaluate and bring relevant information-retrieval and recommender-systems research 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 evaluation and scorecards, reproducible training pipelines, safe deployment, SLOs, observability for tier-1 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 develop team technical depth to reduce reliance on single individuals for critical systems.
Explain technical trade-offs in business terms to product and executive stakeholders, and document decision rationale.
Question existing assumptions and analyze and improve business performance.