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About This Role
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. Join our team, based out of New York City, Chicago and Denver, and help us give our diners the exceptional value they deserve.
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
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.
We look forward to hearing from you! We'll contact you via email or text to schedule interviews and share information about your candidacy.
Salary Context
This $240K-$249K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Wonder, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($244K) sits 14% above the category median. Disclosed range: $240K to $249K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Wonder AI Hiring
Wonder has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in New York, NY, US. Compensation range: $249K - $249K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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