Tesla

Makes electric cars, robotaxis and grid batteries

Data Engineer

Applied Machine LearningPosted on Nov 5

Full-timeFremont, CA, USANo salary listedIn-person

About the Job

Requirements

A degree in Data Science, Artificial Intelligence, Applied Mathematics, or Operations Research, or equivalent experience.
A strong foundation in mathematics, algorithms, and optimization techniques.
Hands-on experience designing, deploying, and scaling enterprise-grade AI and machine learning solutions, including building production-ready models, embedding them into business workflows, and ensuring reliability, performance, and scalability in real-world environments.
Deep expertise in large language models, autonomous AI agents, prompt engineering, and modern AI frameworks such as LangChain and LlamaIndex.
Strong programming proficiency in Python and SQL, with experience using NumPy, pandas, scikit-learn, TensorFlow, and PyTorch.
Hands-on experience developing full-stack applications using Django (Python), Java-based services, and React, with a solid understanding of RESTful API design, integration, and service-oriented architectures.
Knowledge of MLOps best practices, including CI/CD pipelines, model monitoring, containerization, and orchestration tools such as Docker and Kubernetes.

Responsibilities

Build and deploy production-grade multi-echelon inventory optimization to minimize inventory levels, reduce excess and obsolescence, and improve service levels across Tesla's global distribution network.
Develop and integrate Digital Twin capabilities that unify inbound supply, outbound demand, logistics constraints, warehouse capacity, and network topology to enable near-real-time inventory visibility and decision support.
Architect, own, and operate end-to-end production machine learning pipelines, including CI/CD, model versioning, and orchestration, to ensure scalable, modular, reliable, and high-performance inventory optimization systems.
Design and implement intelligent inventory management agents with capabilities such as demand signal detection, stockout risk identification, and exception-based action-routing workflows.
Optimize multi-model architectures that integrate demand sensing, lead time variability, supplier reliability, and service-level constraints to improve forecast accuracy, reduce inventory carrying costs, and support enterprise-scale throughput.
Build and execute large-scale what-if simulations and sensitivity and stress tests to evaluate inventory positioning strategies, quantify impacts on cost, service levels, and warehouse operations, identify supply chain bottlenecks and resiliency gaps, and translate insights into actionable recommendations.
Translate complex quantitative results into clear narratives and visualizations, enabling leadership to make informed tradeoffs between inventory investment, service levels, and operational complexity across the global supply chain.

Category	Supply Chain
Location	Fremont, California
Requisition ID	288907
Type	Full-time

Job Description

<p>We are seeking a highly skilled and driven Data Engineer to develop Inventory Optimization Models that identify strategies for reducing overall inventory levels while minimizing excess and obsolescence. You will be part of the Supply Chain Optimization team that is managing strategic projects and continuous improvement efforts for Tesla’s Global Distribution Network. The role requires the ability to build strong cross-functional working relationships with Material Planning, Logistics, Finance, Warehouse Operations and Service Operations teams. You will require a sharp business focus, a collaborative style of working, and a proactive and critical mindset. You need to acquire deep subject matter knowledge about systems, sourcing, planning and fulfillment processes to build models that drive impactful business changes. It is essential that you can think strategically, connect the dots in the bigger picture, as well as be comfortable in the details of the deliverables to drive operational improvements. The role is expected to simultaneously handle multiple projects of department-level scale and global reach.</p>

Job Responsibilities

Build and deploy production-grade multi-echelon inventory optimization to minimize inventory levels, reduce excess and obsolescence, and improve service levels across Tesla's global distribution network
Develop and integrate Digital Twin capabilities that unify inbound supply, outbound demand, logistics constraints, warehouse capacity, and network topology to enable near-real-time inventory visibility and decision support
Architect, own, and operate end-to-end production ML pipelines, including CI/CD, model versioning, and orchestration, to ensure scalable, modular, reliable, and high-performance inventory optimization systems
Design and implement intelligent inventory management agents with capabilities such as demand signal detection, stockout risk identification, and exception-based action-routing workflows
Optimize multi-model architectures that integrate demand sensing, lead time variability, supplier reliability, and service-level constraints to improve forecast accuracy, reduce inventory carrying costs, and support enterprise-scale throughput
Build and execute large-scale what-if simulations and sensitivity/stress tests to evaluate inventory positioning strategies, quantify impacts on cost, service levels, and warehouse operations, identify supply chain bottlenecks and resiliency gaps, and translate insights into actionable recommendations
Translate complex quantitative results into clear narratives and visualizations, enabling leadership to make informed tradeoffs between inventory investment, service levels, and operational complexity across the global supply chain

Job Requirements

Degree in Data science, AI, Applied Math, Operations Research, or
or equivalent experience
Strong foundation in mathematics, algorithms, and optimization techniques
Proven hands-on experience designing, deploying, and scaling enterprise-grade AI/ML solutions, including building production-ready models, embedding them into business workflows, and ensuring reliability, performance, and scalability in real-world environments
Deep expertise in large language models, autonomous AI agents, prompt engineering, and modern AI frameworks such as LangChain and LlamaIndex
Strong programming proficiency in Python and SQL, with experience using libraries such as NumPy, pandas, scikit-learn, TensorFlow, and PyTorch
Hands-on experience developing full-stack applications using Django (Python), Java-based services, and React, with a solid understanding of RESTful API design, integration, and service-oriented architectures
Knowledge of MLOps best practices, including CI/CD pipelines, model monitoring, and experience with containerization and orchestration tools such as Docker and Kubernetes

About the Company

Tesla

Makes electric cars, robotaxis and grid batteries

Tesla makes electric cars and batteries that store power for homes and utilities, and it develops self-driving software and humanoid robots. In 2023, its Model Y became the first electric vehicle to rank as the world's best-selling car. Buyers order the Model 3 sedan, Model Y SUV, or Cybertruck pickup directly from Tesla instead of through dealers, and they charge on its Supercharger fast-charging network. It also sells Megapack and Powerwall batteries, runs a robotaxi service, and charges drivers a monthly fee for self-driving software that still needs their supervision. Tesla was founded in 2003, with Elon Musk as its lead early investor and a recognized co-founder, and it is based in Austin, Texas.

Company size: 10,001+
Company stage: IPO
Headquarters: Austin, TX
Founded: 2003

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