Physics Informed Machine Learning Jobs in New York

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 an innately curious, business-minded, results-oriented Data Scientist or Machine Learning Engineer to work in our Discovery and Foundation team. We are focused on providing high quality recommendations to diners who are exploring restaurants in their area as well as those who are searching for something specific. We also build models and features that characterize our merchant and menu item corpus. As a member of this highly collaborative team you will partner with other data scientists, engineering, and product to deliver new run time models and services. You will be responsible for creating metrics to validate performance of models, proposing new algorithmic approaches to improve our current system, designing A/B tests, and identifying creative solutions to bridge state of the art information retrieval advanced and business and engineering requirements. Additionally you will be driving technology best practices and guiding the evolution of responsive systems. Some specific responsibilities include but are not limited to creating documentation accessible to both technical and non-technical audiences, mentoring junior data scientists, creating and maintaining automated training jobs, creating metrics dashboards and alerts, advising best algorithmic trade offs to business stakeholders.

Our team practices end to end project ownership and our work focuses heavily on personalized recommendation and classification from content and clickstream. Deep neural networks, transfer learning from pretrained large scale models, classic regressions, fine tuning, and large language models all have a place in our daily lexicon.

The Impact You Will Make

• Help the business gain insights from recommendations in search and discovery with regard to short and long term metrics

• Drive orders and diner returns via enticing and relevant recommendations for searches

• Bring state of the art advances in IR systems to our runtime environment. Assess new algorithms and business policies

• Collaborate with Product and Engineering teams to understand new product ideas, assess risks and ensure that the necessary data is available

• Discover new and innovative ways to refine what we're doing and question existing assumptions.

• Relentlessly analyze and improve the performance of our business.

What You Bring to the Table

• MS/PhD in quantitative discipline (Computer Science, Math, Physics, Engineering, Statistics or other technical field etc) or equivalent experience

• 4+ years experience with data analytics, machine learning, or related field

• 2+ years experience in applied predictive modeling with TensorFlow

• 2+ years experience in information retrieval or recommendation systems

• Experience with language models, especially on imperfect grammars

• Experience with Large Language Models (LLMs), including fine-tuning and deploying transformer-based architectures in real-world applications

• Experience tuning runtime models using GPUs

• Experience in data engineering and feature preparation in pyspark, hive,and the python data stack.

• Comfort communicating performance metrics, model details, and features specifications to technical and non-technical audiences

• Ability to keep up with the latest publications and synthesize research into working models

• 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: $176,000 - $191,000 per year.

Illinois: $158,500 - $172,000 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

We offer a competitive salary package including equity and 401K. Additionally, we provide multiple medical, dental, and vision plans to meet all of our employees' needs as well as many benefits and perks that are not listed.

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.