Rivian
About Rivian
Rivian is on a mission to keep the world adventurous forever. We build emissions‑free electric adventure vehicles and seek curious, courageous souls to join our team. We constantly challenge what’s possible, reframing old problems, seeking new solutions, and operating comfortably in unknown areas. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations.
Role Summary We are seeking a talented and driven Sr. Machine Learning Engineer to join our dynamic Supply Chain team. This exciting opportunity is ideal for someone passionate about applying machine learning, data science, and data engineering principles to solve complex real‑world problems in a fast‑paced automotive environment. You will develop, deploy, and maintain ML‑powered solutions that enhance the efficiency, resilience, and intelligence of our supply chain.
Responsibilities
Design and Develop ML Models: Collaborate with stakeholders to understand supply chain challenges and design, develop, and implement machine learning models (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, including automated 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 pipelines to automate the testing, validation, and deployment of models and underlying infrastructure.
Collaboration & Communication: Work closely with data scientists, data engineers, supply chain experts, and cross‑functional teams to translate business requirements into technical solutions and communicate findings effectively.
Advanced Monitoring & Observability: Implement comprehensive monitoring solutions for both model performance (accuracy, drift, bias) and operational health (latency, throughput, error rates, cost) with automated alerting and response strategies.
Infrastructure as Code (IaC): Build and manage scalable ML infrastructure on cloud platforms using IaC principles and tools (Terraform, Databricks DABs).
Research & Innovation: Stay current with the latest advancements in machine learning, data science, and supply chain technologies, and explore new approaches to solve emerging challenges.
Qualifications
Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Operations Research, Statistics, or a related quantitative field.
Experience: 3–5 years of practical experience in machine learning engineering, data science, or data engineering roles.
Technical Skills: 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 (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 (Docker, Kubernetes, CI/CD pipelines like Jenkins, GitLab CI, GitHub Actions) for infrastructure as code and automated deployments.
Experience with Infrastructure as Code tools (Terraform, Databricks DABs) highly desirable.
Experience with version control systems (Git).
Data Science Fundamentals: Solid understanding of statistical modeling, machine learning algorithms, and experimental design.
Data Engineering Acumen: Basic understanding of data warehousing concepts, ETL processes, and data governance.
Problem‑Solving: Excellent analytical and problem‑solving skills with a keen eye for detail.
Domain Interest: Genuine interest in supply chain operations and the automotive industry, particularly electric vehicles.
Communication: Ability to clearly articulate complex technical concepts to both technical and non‑technical audiences.
Bonus Points (Nice To Have)
Experience with supply chain optimization, logistics, or manufacturing data.
Knowledge of simulation or optimization software.
Pay Disclosure
Salary Range for California Based Applicants: $149,800 – $203,500.
Salary Range for Illinois & Michigan Based Applicants: $143,300 – $179,100.
Benefits Summary Rivian provides robust medical, Rx, dental, and vision insurance packages for full‑time employees, their spouse or domestic partner, and children up to age 26. Coverage is effective on the first day of employment, and Rivian covers most of the premiums.
Equal Opportunity Rivian is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender, gender expression, gender identity, genetic information, or characteristics protected by law. Rivian is committed to ensuring that our hiring process is accessible for persons with disabilities. If you have a disability or limitation, please email us at candidateaccommodations@rivian.com.
Candidate Data Privacy Rivian may collect, use, and disclose your personal information or personal data (within the meaning of applicable data protection laws) when you apply for employment and participate in our recruitment processes. This data includes contact, demographic, communications, educational, professional, employment, social media/website, network/device, recruiting system usage/interaction, security, and preference information. Rivian may use your candidate personal data for tracking interactions, improving applications, assessing employment qualifications, conducting reference checks, establishing employment relationships, complying with legal obligations, record‑keeping, ensuring network security, and preventing fraud, as permitted by law. Rivian may share your data with internal personnel, affiliates, and service providers, and may transfer or store it internationally. We may not accept applications from third‑party application services.
Note Please note that we are currently not accepting applications from third party application services.
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Role Summary We are seeking a talented and driven Sr. Machine Learning Engineer to join our dynamic Supply Chain team. This exciting opportunity is ideal for someone passionate about applying machine learning, data science, and data engineering principles to solve complex real‑world problems in a fast‑paced automotive environment. You will develop, deploy, and maintain ML‑powered solutions that enhance the efficiency, resilience, and intelligence of our supply chain.
Responsibilities
Design and Develop ML Models: Collaborate with stakeholders to understand supply chain challenges and design, develop, and implement machine learning models (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, including automated 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 pipelines to automate the testing, validation, and deployment of models and underlying infrastructure.
Collaboration & Communication: Work closely with data scientists, data engineers, supply chain experts, and cross‑functional teams to translate business requirements into technical solutions and communicate findings effectively.
Advanced Monitoring & Observability: Implement comprehensive monitoring solutions for both model performance (accuracy, drift, bias) and operational health (latency, throughput, error rates, cost) with automated alerting and response strategies.
Infrastructure as Code (IaC): Build and manage scalable ML infrastructure on cloud platforms using IaC principles and tools (Terraform, Databricks DABs).
Research & Innovation: Stay current with the latest advancements in machine learning, data science, and supply chain technologies, and explore new approaches to solve emerging challenges.
Qualifications
Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Operations Research, Statistics, or a related quantitative field.
Experience: 3–5 years of practical experience in machine learning engineering, data science, or data engineering roles.
Technical Skills: 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 (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 (Docker, Kubernetes, CI/CD pipelines like Jenkins, GitLab CI, GitHub Actions) for infrastructure as code and automated deployments.
Experience with Infrastructure as Code tools (Terraform, Databricks DABs) highly desirable.
Experience with version control systems (Git).
Data Science Fundamentals: Solid understanding of statistical modeling, machine learning algorithms, and experimental design.
Data Engineering Acumen: Basic understanding of data warehousing concepts, ETL processes, and data governance.
Problem‑Solving: Excellent analytical and problem‑solving skills with a keen eye for detail.
Domain Interest: Genuine interest in supply chain operations and the automotive industry, particularly electric vehicles.
Communication: Ability to clearly articulate complex technical concepts to both technical and non‑technical audiences.
Bonus Points (Nice To Have)
Experience with supply chain optimization, logistics, or manufacturing data.
Knowledge of simulation or optimization software.
Pay Disclosure
Salary Range for California Based Applicants: $149,800 – $203,500.
Salary Range for Illinois & Michigan Based Applicants: $143,300 – $179,100.
Benefits Summary Rivian provides robust medical, Rx, dental, and vision insurance packages for full‑time employees, their spouse or domestic partner, and children up to age 26. Coverage is effective on the first day of employment, and Rivian covers most of the premiums.
Equal Opportunity Rivian is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender, gender expression, gender identity, genetic information, or characteristics protected by law. Rivian is committed to ensuring that our hiring process is accessible for persons with disabilities. If you have a disability or limitation, please email us at candidateaccommodations@rivian.com.
Candidate Data Privacy Rivian may collect, use, and disclose your personal information or personal data (within the meaning of applicable data protection laws) when you apply for employment and participate in our recruitment processes. This data includes contact, demographic, communications, educational, professional, employment, social media/website, network/device, recruiting system usage/interaction, security, and preference information. Rivian may use your candidate personal data for tracking interactions, improving applications, assessing employment qualifications, conducting reference checks, establishing employment relationships, complying with legal obligations, record‑keeping, ensuring network security, and preventing fraud, as permitted by law. Rivian may share your data with internal personnel, affiliates, and service providers, and may transfer or store it internationally. We may not accept applications from third‑party application services.
Note Please note that we are currently not accepting applications from third party application services.
#J-18808-Ljbffr