Staff Machine Learning Engineer

Grubhub Holdings Inc.; New York (NY); On-site; USD 209,000 - 225,000; Full time

Benefits offered by this job

Equity

401(k) plan

Medical insurance

Dental insurance

Vision insurance

Short/Long-term disability

Paid time off

Paid parental leave

Discounted meals

Job summary

Grubhub Holdings Inc. is seeking a Staff Machine Learning Engineer in New York to own the architecture of the ranking and recommendation stack, from candidate retrieval to multi-objective ranking.

You will drive long-term diner value, integrate IR and LLM research into live systems, and raise cross-team engineering standards with robust experimentation and observability.

Qualifications

• MS/PhD in a quantitative field or equivalent experience.
• 6+ years building and shipping ML systems, with 3+ years operating at senior scope.
• Deep experience in recommendation systems, ranking, or information retrieval at scale.
• Production deep learning with TensorFlow or PyTorch, including training and serving on GPUs.
• Experience with Large Language Models and transformer architectures, including fine-tuning.
• Strong data engineering fundamentals: PySpark, Hive/SQL, Python data stack, and feature pipelines.
• Experimentation: designing A/B tests and proper guardrails.
• Cloud ML infrastructure (AWS/SageMaker or equivalent) and monitoring.
• Demonstrated technical leadership: mentoring engineers and influencing decisions.
• Comfort communicating metrics and trade-offs to technical and executive audiences.

Responsibilities

• Own the architecture of the ranking and recommendation stack end to end.
• Lead evolution of optimization objectives toward long-term diner value.
• Bring state-of-the-art IR and recommender research into runtime (LLMs, embedding retrieval).
• Raise engineering standards: evaluation, pipelines, observability, and deployment safety.
• Partner with product, search, data platform; mentor engineers; contribute to hiring.

Skills

ML system design

Recommendation systems

Information retrieval

TensorFlow / PyTorch

LLMs

Data engineering

Experimentation

Cloud ML infrastructure

Technical leadership

Communication of metrics

Education

MS/PhD in CS/Math/Physics/Engineering/Statistics

Tools

PySpark

Hive

SQL

SageMaker

AWS

Job description

About the OpportunityGrubhub 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.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 MakeOwn 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 TableMS/PhD in a quantitative discipline (Computer Science, Math, Physics, Engineering, Statistics or other technical field) or equivalent experience6+ years building and shipping machine learning systems, including 3+ years operating at senior-level scope: setting technical direction across multiple teams, model families, or systemsDeep experience in recommendation systems, ranking, or information retrieval at scale, in production and under real latency and cost constraintsProven track record with production deep learning in TensorFlow or PyTorch, including training, serving, and tuning runtime models on GPUsExperience 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 plusStrong data engineering fundamentals: PySpark, Hive/SQL, the Python data stack, and feature pipelines you can debug as well as buildFluency with experimentation: designing A/B tests, choosing the right guardrails, and recognizing when an offline metric is misleading youExperience with cloud ML infrastructure (AWS/SageMaker or equivalent), model deployment, and production monitoring and observabilityDemonstrated technical leadership: mentoring engineers, driving design and architecture reviews beyond your own team, and influencing decisions without direct authorityComfort communicating performance metrics, model behavior, and technical trade-offs to both deeply technical and non-technical audiences, up to and including executive stakeholdersAbility to keep up with the latest research publications and synthesize them into working production systemsDeep interest in self-motivated continuous learningOur 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.BenefitsThe 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 NoteAt 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.We look forward to hearing from you! We'll contact you via email or text to schedule interviews and share information about your candidacy.