Senior Software Engineer - Machine Learning Infrastructure

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 skilled and innovative Software Engineer with expertise in Machine Learning Infrastructure to join our Top of the Funnel Discovery team, powering our Search, Recommendations and Ads platforms. In this role, you will be responsible for bridging the gap between data science and production systems by taking models from development in Python notebooks to efficient, scalable, and performant artifacts. You will work closely with data scientists across various teams, ensuring that models are not only accurate but also optimized for runtime performance along with building out appropriate feature store pipelines. Your work will extend beyond a single domain, impacting a wide range of models across our platform. Experience with SageMaker or building internal machine learning deployment platforms will be highly valued as you help us streamline and enhance our ML operations. Additionally, there will be a deep focus on observability, reliability, and scalability, ensuring that our systems can handle the complexities of real-world data and serve high traffic volumes across the country.  

The Impact You Will Make:

• Lead the design and implementation of scalable and reliable MLOps pipelines. You'll ensure that our data scientists can rapidly iterate on models and deploy them seamlessly into production.

• Oversee the migration and management of monorepos, ensuring efficient dependency management, security compliance, and streamlined workflows across Python projects.

• Implement robust monitoring frameworks to ensure that our systems are observable and that we can react swiftly to any issues. You'll play a critical role in minimizing SEV incidents and ensuring system health.

• Drive continuous improvements in our data science platform, exploring how we can integrate tools (eg. Sagemaker) in the data science workflow and integrate with the rest of the GrubHub platform.

• Collaborate closely with data scientists to instill best practices in coding, testing, and version control. Raise the overall quality and reliability of our codebase.

• Develop and refine systems for data versioning, model management, and deployment strategies, ensuring that models are easy to manage, debug, and deploy.

• Data engineering skills to write PySpark or Scala Spark jobs that efficiently pull data from our data lakes into feature stores, ensuring that the necessary data is readily available for model training and deployment is an added plus.

What You Bring To The Table:

• Master’s or Bachelor’s Degree in Science, Programming or Engineering related field.

• 4+ years in software engineering with a focus on MLOps, Python, and cloud-based environments (AWS preferred).

• Proven experience in building and maintaining CI/CD pipelines, managing monorepos, and scaling machine learning models in production.

• Experience in AWS offerings like SageMaker and Bedrock, and experience with vector stores is a plus.

• Strong proficiency and experience in Python, containerization, orchestration tools, and experience with data versioning and model management tools.

• Experience in data engineering, including writing PySpark jobs to extract, transform, and load data is a plus.

• Ability to tackle complex problems related to system integration, data consistency, and infrastructure scaling.

• Experience with distributed systems and microservices architecture is a plus.

• Familiarity with eventually consistent databases (Cassandra) and experience with ElasticSearch is a plus.

• Excellent communication skills, with the ability to work closely with data scientists, product managers, and oth