About Store No8
We are the incubation arm of Walmart. Store No8 was formed in 2017 as part of Walmart’s larger innovation mission to shape the future of commerce. We pursue big ideas and take risks by stepping outside of Walmart’s core business to focus on leapfrog capabilities across conversational commerce, mixed reality, in-store digitization, and more. Our ultimate goal: fuel Walmart’s core business, create new operational efficiencies, and unlock amazing experiences for our customers in the long-term.
This team within Store Nº8 is working on healthcare innovations because Walmart’s promise is to help its customers save money and live better. This includes building products that enhance both their physical and emotional wellbeing.
About the Role
As a Senior MLOps and DevOps Engineer for one of our Store Nº8’s portfolio companies, you will focus on building ML pipelines and end-to-end lifecycle management within our incubator. Reporting directly to the Head of Engineering, you will partner with data science, product, Health & Wellness, store operations, and legal teams. You will need to be a self-starter with a bias for action and excellent communication skills.
This is a rare opportunity, moving at the speed of a startup, with the backing and in-house data (lots of data) of the Fortune 1 company. You will bridge the art of what is possible across today’s rapidly evolving healthcare data and informatics landscape.
What You’ll Do
Design and build MLOps systems, including data pipelines and production-level machine learning (ML) infrastructure, using tools such as Kubeflow Pipelines and Apache Beam.
Build out and monitor CI/CD and DevOps pipelines for automated image builds, running of tests, and deployment of models and using Jenkins, Github, container technologies.
Deploy Code Build artifacts, containers to production under the constraints of scalability, correctness, security and maintainability.
Deploy ML models to production under the constraints of scalability, correctness, and maintainability.
Create monitoring systems to detect data drift, track model versions, report on production model performance, and alert the team of any anomalous model behavior.
Optimize and give feedback to research-level models to bring them to production level.
Build tooling to ingest data from and write it to different storage locations such as BigQuery, SQL databases or GCP Cloud Storage.
Leverage your experience to drive best practices in ML systems and data engineering.
Collaborate with cross functional teams of data scientists and data engineers in building ML infrastructure that supports the needs.
Design infrastructure to run programmatic A/B tests on production models and easily consume insights.
Keep thorough documentation of all MLOps and DevOps systems and train team members as needed.
What You’ll Need
2+ years of machine learning product development experience, using state-of-the-art tooling.
Advanced proficiency with Python and SQL. Ability to create abstractions, APIs, and libraries.
Familiarity and knowledge of ML frameworks/libraries (e.g., scikit-learn, Pytorch, Tensorflow, transformers), data structures, data modeling, and software architecture.
Strong background in cloud computing and distributed systems.
Experience with model management registries and lineage (training data, configuration, model parameters, etc.).
Deployment management and monitoring: provisioning/orchestration, CI/CD, Jenkins, Github, real-time and batch inference, outlier/anomaly detection, data drift monitoring.
Production experience leveraging at least one cloud provider - e.g., AWS, GCP, Azure.
Experience with Docker and Kubernetes. Familiarity with popular MLOps tooling from cloud vendors like GCP (Vertex AI), AWS (SageMaker) or Azure Machine Learning and MLFlow, Kubeflow, etc.
Experience with Infrastructure-as-code development (e.g., Terraform, Cloud Formation, Ansible, Chef, Bash scripting, etc.).
Superb analytical and problem-solving abilities.
Excellent communication and collaboration skills.
A plus if you also have
Experience using DAGs to define data pipelines, e.g., Airflow, Luigi, Dagster, Prefect, Kubeflow.
Experience with BigQuery, Spark, Kafka and dbt.
Proficiency with asynchronous python frameworks such as Fast API.
Experience developing ML models
Option 1: Bachelor’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology or related field and 3 years' experience in an analytics or related field.
Option 2: Master’s degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 1 years' experience in an analytics or related field.
Option 3: 5 years' experience in an analytics or related field.
engineering or related field.
850 Cherry Avenue, San Bruno, CA 94066-3031, United States of America