Lead Machine Learning Engineer

Lead Machine Learning Engineer

About Us:

Cart.com is an ecommerce software and services company on a mission to democratize ecommerce and give digital merchants the freedom to grow. We are integrating all the pieces of the ecommerce value chain brands need to thrive, creating a truly end-to-end Ecommerce-as-a-Service platform that helps third party brands move faster, grow more quickly, and deliver on their promises more completely.  

As a the Lead Infrastructure Engineer, you will be responsible for managing the systems, services, MLOps, and production models for one of our core departments that interfaces across our entire suite of “ecommerce-as-a-service" products.  This role is responsible for people as well as technical leadership - and will play a huge role in building out the future of commerce as we know it! If you want to get in early on a company that is going to disrupt the booming ecommerce space, then this is your role! We’re looking for innovators who are passionate on working in a fast-paced and collaborative environment. So if this is you, then read on:


    • Study and transform data science prototypes
    • Design machine learning systems
    • Construct the environment to run these ML systems in AWS
    • Research and implement appropriate ML algorithms and tools
    • Develop machine learning applications according to requirements
    • Select appropriate datasets and data representation methods
    • Run machine learning tests and experiments
    • Perform statistical analysis and fine-tuning using test results
    • Train and retrain systems when necessary
    • Extend existing libraries and frameworks
    • Keep abreast of developments in the field

ML Knowledge Requirements:

    • Kafka
    • - Ingesting data from core product before Spark processes them
    • Spark (pyspark, MLLib)
    • - Random forest streaming application for automapping internal fields
    • Jupyter (Spark backend)
    • - Running experiments & model training
    • Git/GitHub
    • - Source control, versioning artifacts like automapping random forest models
    • Terraform
    • - Infrastructure as Code automation of deployments

Cloud Knowledge Requirements

    • AWS EMR
    • - Managed spark cluster configured for cost-effective auto-scaling during traffic spikes
    • AWS Lambda + API Gateway
    • - PUT/GET endpoints for core product to communicate with Kafka and receive automapping results
    • AWS DynamoDB
    • - Temporarily (up to 1 week) store automapping results for core product to pick up at the earliest convenience
    • AWS VPC + VPN Gateway
    • - Private secure VPC configured to communicate with Hetzner hosted systems over a VPN tunnel

Data Collection Related Projects:

    • AWS DMS
    • - Collecting historical MySQL data via continous DB replication
    • AWS S3 + Glue + Athena/Tableau
    • - Events data lake for analytics team to access easily via Athena/Tableau
    • AWS ECS + Amazon SP API
    • - Scraping product data from Amazon


    • Proven experience as a Machine Learning Engineer or similar role
    • Understanding of data structures, data modeling and software architecture
    • Deep knowledge of math, probability, statistics and algorithms
    • Ability to write robust code in Python, Java and R
    • Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
    • Excellent communication skills
    • Ability to work in a team
    • Outstanding analytical and problem-solving skills
    • BSc in Computer Science, Mathematics or similar field; Master’s degree is a plus

These aren’t just buried somewhere in an employee manual. We live and breathe them. They are on the walls and live in our hearts. They come up constantly in conversations and actions. They govern the decisions of the newest hire all the way up to our CEO:
We live for brands and are fanatical about their success.
We explore new ideas and discover creative solutions. We think openly about how to serve brands and solve problems.
No one expected this to be easy. We are resilient— we dig in and keep going.
Every person here has an obligation to question norms, voice concerns, and offer their perspective.
We work with integrity and respect, ask for help, and extend the same help to others.
Our people are our biggest strength. We have fun and make real connections with one another and with the brands we serve.

Cart.com is deeply committed to building a diverse and inclusive workplace. We’re proud to be an equal opportunity employer, seeking to identify and onboard people from all walks of life. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, family status, marital status, sexual orientation, national origin, genetics, neurodiversity, disability, age, or veteran status, or any other non-merit based or legally protected grounds.

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