Staff Machine Learning Engineer

GrubHub Inc.; New York (NY); Hybrid; USD 209,000 - 225,000; Full time

Benefits offered by this job

Equity

401(k)

Medical, dental, and vision

Disability insurance

Paid time off

Parental leave

Discounted meals

Job summary

Wonder is seeking a Staff Machine Learning Engineer in New York to lead the ranking, retrieval, and recommendation engine powering Discovery. You will architect end-to-end systems, drive long-term diner value, and bring state-of-the-art IR and recommender research into production.

You will mentor engineers, influence cross-functional teams, and translate technical trade-offs into business terms for executives, while maintaining high standards in deployment, security, and observability.

Qualifications

• MS/PhD in a quantitative discipline or equivalent experience.
• 6+ years building and shipping ML systems, including 3+ years at senior scope.
• Deep experience in recommendation/ranking/info retrieval at scale.
• Production DL with TensorFlow or PyTorch (training, serving, tuning).
• Experience with LLMs and transformer architectures in latency-sensitive apps.
• Strong data engineering fundamentals: PySpark, Hive/SQL, Python stack.
• Fluency with experimentation and A/B testing.
• Cloud ML infra, deployment, monitoring and observability.

Responsibilities

• Own architecture of ranking and recommendation stack end-to-end.
• Lead optimization objective evolution toward long-term diner value.
• Bring state-of-the-art IR and recommender research into runtime systems.
• Raise engineering and operational standards across teams.
• Partner with product, search engineering, ads, and data platform to shape roadmaps.
• Mentor senior and mid-level engineers and participate in hiring.
• Translate trade-offs into business terms for stakeholders.
• Continuously analyze and improve model performance.

Skills

Architecture design

Deep learning systems

Recommendation systems

TensorFlow/PyTorch

Large Language Models

PySpark/Hive/SQL

Experimentation

Cloud ML infra

Mentoring

Education

MS/PhD in quantitative field or equivalent

Tools

TensorFlow

PyTorch

AWS/SageMaker

PySpark

Hive/SQL

Job description

About The Opportunity

Grubhub is looking for a 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.

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
• 6+ years building and shipping machine learning systems, including 3+ years operating at senior-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.

New York: $208,500 - $225,000 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

The benefits applicable to this role include a competitive compensation package with equity and a 401(k). We also offer 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. To learn more about benefits for this role, visit our Careers page here.

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.