Rivian is looking to enhance the efficiency, resilience, and intelligence of its supply chain by applying machine learning, data science, and data engineering principles.
Requirements
Strong programming proficiency in Python.
Experience with machine learning frameworks/libraries such as scikit-learn, TensorFlow, PyTorch.
Solid understanding of relational databases (SQL) and experience with big data technologies (e.g., Spark, Hadoop, Snowflake, Databricks).
MLOps Platforms: Hands-on experience building production ML systems using tools like MLflow, Kubeflow, Vertex AI, or SageMaker for experiment tracking, model registry, and serving.
Familiarity with cloud platforms (Databricks, AWS, Azure, GCP) and their relevant services for ML and data.
Experience with DevOps principles and tools (e.g., Docker, Kubernetes, CI/CD pipelines like Jenkins, GitLab CI, GitHub Actions) for infrastructure as code and automated deployments.
Experience with Infrastructure as Code tools (e.g., Terraform, Databricks DABs) is highly desirable.
Responsibilities
Design and Develop ML Models: Collaborate with stakeholders to understand supply chain challenges and design, develop, and implement machine learning models (e.g., forecasting, optimization, anomaly detection, predictive maintenance) to address them.
Data Preparation & Feature Engineering: Work with large, complex datasets from various sources, performing data cleaning, transformation, and feature engineering to prepare data for model training and deployment.
Model Deployment & MLOps: Design, build, and maintain robust MLOps pipelines to effectively deploy, monitor, and manage machine learning models in production environments. This includes setting up automated model retraining, versioning, and performance tracking.
Data Pipeline Development: Contribute to the development and optimization of scalable data pipelines to ingest, process, and store supply chain data, ensuring data quality and accessibility for ML applications.
CI/CD for Machine Learning: Design, implement, and maintain CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines to automate the testing, validation, and deployment of models and the underlying infrastructure.
Advanced Monitoring & Observability: Implement comprehensive monitoring solutions for both model performance (accuracy, drift, bias) and operational health (latency, throughput, error rates, cost).
Infrastructure as Code (IaC): Build and manage scalable ML infrastructure on cloud platforms using IaC principles and tools (e.g., Terraform, Databricks DABs).
Other
3-5 years of practical experience in machine learning engineering, data science, or data engineering roles.
Solid understanding of statistical modeling, machine learning algorithms, and experimental design.
Basic understanding of data warehousing concepts, ETL processes, and data governance.
Excellent analytical and problem-solving skills with a keen eye for detail.
Genuine interest in supply chain operations and the automotive industry, particularly electric vehicles.
