Senior Staff Machine Learning Engineer

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

About Grubhub

At Grubhub, we believe food is more than just a meal: It’s a source of discovery, connection, and pure enjoyment. There’s a time and place for every type of dish, from hidden neighborhood gems to tried‑and‑true favorites, and we exist to connect people with the food they love in all the ways they like to dig in. We’ve been at it since 2004, but now, as part of Wonder, Grubhub is operating with a renewed sense of momentum and the high‑velocity energy of a powerhouse startup. As a leading U.S. ordering and delivery marketplace, we feature over 415,000 merchants in more than 4,000 cities, creating the ultimate food experience by elevating online ordering through innovative restaurant technology, easy‑to‑use platforms, and an improved delivery experience. We are constantly finding new ways to innovate—from integrated grocery delivery with groceries powered by Instacart to exclusive loyalty programs.

About the Opportunity

Grubhub is looking for a Senior Staff Machine Learning Engineer to help lead the machine learning engine behind Discovery: the ranking, recommendation, and retrieval systems that decide what every diner sees when they open the app or run a search. These models sit on the critical path to conversion for hundreds of thousands of merchants and hundreds of millions of menu items, and they are one of the largest organic growth levers we have.

This is a hands‑on technical leadership role, not a management role. You will own model architecture across several connected technical areas: search ranking, homepage and topic recommendations, retrieval, and query understanding. You will be accountable for how those pieces fit together, not only for any single model. You will set technical direction alongside other staff engineers, product managers, and platform partners.

You will raise the bar on how the organization builds, evaluates, and operates models, and mentor the engineers around you through design reviews, code reviews, and direct feedback. We expect you to challenge technical decisions across teams when the engineering case is clear, and to bring evidence when you do. Our team practices end‑to‑end project ownership, and our work focuses heavily on personalized recommendation, retrieval, and classification from catalog content and clickstream. Deep neural networks, learned embeddings and approximate nearest neighbor retrieval, transfer learning from pre‑trained large scale models, calibration, classic regressions, fine tuning, and large language models all have a place in our daily lexicon.

The Impact You Will Make

Own the architecture of our ranking and recommendation stack end‑to‑end: candidate retrieval, multi‑objective ranking, calibration, and the ensemble that trades conversion against profitability. Make the cross‑system design calls that no individual model owner can make alone. Lead the evolution of our optimization objective from short‑term conversion toward long‑term diner value, including the offline evaluation and online experimentation work required to trust the result before it ships. Bring state‑of‑the‑art research in information retrieval and recommender systems into our runtime environment: LLM‑driven query and intent understanding, embedding‑based retrieval, sequential user representations for cold start, and real‑time inference.

Assess rigorously what actually transfers to our traffic, and say no to what does not. Raise engineering and operational standards across multiple teams: model evaluation and scorecards, reproducible training pipelines, safe deployment, SLOs and observability for tier‑1 models, and proactive management of technical debt before it becomes urgent. Partner with Product, Search Engineering, Ads, and Data Platform to shape roadmaps, surface risk early, and make sure the data and infrastructure exist before the model needs them. Mentor senior and mid‑level engineers, participate in hiring, and grow the technical depth of the team so that no critical system depends on a single person. Translate technical trade‑offs into business terms for product and executive stakeholders, and document the rationale clearly enough that decisions outlive the people who made them. Question existing assumptions, look for the innovation we are not yet pursuing, and relentlessly analyze and improve the performance of our business.

What You Bring to the Table

• 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: setting technical direction across multiple teams, model families, or systems
• Deep experience in recommendation systems, ranking, or information retrieval at scale, in production and under 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 deploying them in latency‑sensitive applications.
• Experience with language understanding over imperfect grammar (real‑world search queries, menu and catalog text) is a strong plus
• Strong data engineering fundamentals: PySpark, Hive/SQL, the Python data stack, and feature pipelines you can debug as well as build
• Fluency with experimentation: designing A/B tests, choosing the right guardrails, and recognizing when an offline metric is misleading you
• Experience with cloud ML infrastructure (AWS/SageMaker or equivalent), model deployment, and production monitoring and observability
• Demonstrated technical leadership: mentoring engineers, driving design and architecture reviews beyond your own team, and influencing decisions without direct authority
• Comfort communicating performance metrics, model behavior, and technical trade‑offs to both deeply technical and non‑technical audiences, up to and including executive stakeholders
• Ability to keep up with the latest research publications and synthesize them into working production systems
• Deep interest in self‑motivated continuous learning
• Our hybrid model requires 3 days a week in the office. That said, many team members choose to come in more often to take advantage of in‑person collaboration and connection. You're welcome—and encouraged—to be in the office up to 5 days a week if it works for you.

#LI‑Hybrid

New York: $240,000 - $249,500 per year.

Wonder uses geographic‑specific salary structures, which means the salary offered may vary depending on where the job is located. The final salary offer will take into account various factors, such as the candidate's skills, education, training, credentials, and experience.

Benefits

• a competitive compensation package with equity and a 401(k)
• a choice of medical, dental, and vision plans, company paid short and long term disability coverage, paid time off including flexible time off for exempt employees, paid vacation for non‑exempt employees, and paid sick leave in compliance with applicable law in addition to paid parental leave, discounted meals and exclusive perks across the Wonder family of brands

Eligibility, effective dates, and available plan options vary by employment classification and location.

A Final Note

At Wonder, we build the best teams by hiring with an objective lens — evaluating people for their potential while championing diversity, equity, and inclusion. We do not discriminate based on race, color, religion, gender identity or expression, sexual orientation, national origin, age, military service eligibility, veteran status, marital status, disability, or any other protected class. As part of our commitment to fair and compliant hiring practices, Wonder participates in the federal government's E-Verify program to confirm employment eligibility. If you need an accommodation during the interview process, please let your recruiter know.

Join Our Team

At Grubhub, we champion restaurants from coast to coast. Restaurants sit at the heart of communities. It’s our mission to strengthen their roots, deepen their connections, and increase the positive impact they have on people and society. Grubhub, part of Wonder, delivers the best local, authentic cuisine right to diners’ doors—and new customers and billions in revenue to local businesses. Featuring over hundreds of thousands of merchants in over 4,000 cities nationwide, our innovative technology, user‑friendly platforms, and streamlined delivery capabilities have made us an industry leader in the world of online food ordering. Since we opened our doors in 2004, Grubhub has been opening doors all across the country. Bakery doors in Hyde Park, jibarito joint doors in Queens, and doors of opportunity all across the country. Join our team and help us open more.