Now, more than ever, the Toast team is committed to our customers. We’re taking steps to help restaurants navigate these unprecedented times with technology, resources, and community. Our focus is on building the restaurant platform that helps restaurants adapt, take control, and get back to what they do best: building the businesses they love. And because our technology is purpose-built for restaurants, by restaurant people, restaurants can trust that we’ll deliver on their needs for today while investing in experiences that will power their restaurant of the future.
Bready* to make a change?
Toast is looking for a machine learning engineer to bring data science capabilities into the Toast platform. You will work with engineers, data scientists and product managers to turn machine learning models into business impact across product lines, including fintech, menu recommendations, ingredient and menu taxonomy, item classifications and customer support through generative AI etc. We need your help to create the infrastructure that enables data scientists to build, release, and monitor models at scale.
About this Roll*:
- Apply MLOps expertise internally to help further define and improve the capabilities of Toast’s ML pipelines to increase automation, repeatability, and robustness of our ML practices
- Build ML infrastructure for full model development lifecycle (training, versioning, monitoring, etc)
- Explore and evaluate new ML-related technologies to optimize model performance for latency, availability, and accuracy in collaboration with data scientists and ML engineering team members
- Develop APIs and libraries to deliver machine learning artifacts into production environments
- Help execute on the architectural vision to unlock data science across the entire Toast platform
Do you have the right ingredients*?
- Up to three year of software development experience with understanding of the ML development lifecycle
- Knowledge of how machine learning solutions are shipped in production environments
- Knowledge of tools and best practices for developing model deployment pipelines
- Experience with microservice-based architecture, preferably with AWS tooling (SageMaker, DynamoDB, Athena, etc.)
- Familiarity with any of the following languages (Java/Kotlin, Python), ML frameworks (scikit-learn, Tensorflow, PyTorch) and distributed computing frameworks (Spark, Ray, Dask)
- Experience in software engineering best practices and tools including object-oriented programming, test-driven development, CI/CD, git, shell scripting, task orchestration (Airflow)
Bonus ingredients*:
- Experience with workflow orchestration tools like Apache Airflow and an open-source infrastructure-as-code tool such as Terraform is a plus
- Foundational knowledge in statistical concepts (e.g. classification, regression, etc) and deep learning algorithms (e.g. CNN, RNN) is desirable
