Manager, Engineering - Machine Learning

About The Opportunity
We’re all about connecting hungry diners with our network of over 300,000 restaurants nationwide. Innovative technology, user-friendly platforms and streamlined delivery capabilities set us apart and make us an industry leader in the world of online food ordering. When you join our team, you become part of a community that works together to innovate, solve problems, grow, work hard and have a ton of fun in the process!

Why Work For Us

Grubhub is a place where authentically fun culture meets innovation and teamwork. We believe in empowering people and opening doors for new opportunities. If you’re looking for a place that values strong relationships, embraces diverse ideas–all while having fun together–Grubhub is the place for you!

Grubhub is looking for a Machine Learning Manager to join us and support the customer side of our e-commerce business.  This role will own foundational projects to classify and characterize our menu item and merchant corpus.  They will use these to surface high quality food recommendations to customers.  

Our team is a talented group of smart, humble data scientists who are passionate about creating amazing products through innovation and strong engineering.  This team has end to end ownership of the model development lifecycle and deployment process.  With leadership of this team you will be responsible for shaping the team, providing guidance on career growth, mentoring, designing ML algorithms and architecture, and influencing our product vision.  Within grubhub, you will be directly involved with building new features to benefit existing and potential users in a highly collaborative environment of other ML teams, backend engineers, and product managers.  

The Impact You will Make:

• Manage a team of data scientists and oversee their career progression and planning.

• Communicate with stakeholders, product owners, other technologists, and users to create machine learning solutions based on an understanding of business and technical priorities.

• Participate in future product direction and help build engineering roadmap.

• Build models that integrate with real time and offline data sources in existing and new services.  This work will continue to drive the diner recommendation experience towards relevance and enticement.

• Bring state of the art advances in IR systems, large language models, evaluation metrics to our runtime environment and batch processing. Assess new algorithms and libraries for incorporation into our stack.

• Actively contribute to the adoption of strong software architecture, development best practices and new technologies. We are always improving the process of building software; we’ll need your help to do that.

• Coach junior team members how to execute on an end to end project and design useful long term monitors.

• Work extensively with data engineering to optimize and identify new opportunities.

• Act as a technical voice in the Engineering organization

• Design, implement, deliver, and test features in our application while understanding our products from both a technical and business perspective

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

• Creatively solve complex technical problems for all our Grubhub and Seamless brands

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 language modeling, especially on imperfect grammars

• 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

• Experience deploying machine learning models to production

• Ability to keep up with the latest p