Tesla

Makes electric cars, robotaxis and grid batteries

Software Engineer - Machine Learning Operations, Manufacturing Quality

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeFremont, CA, USANo salary listedIn-person

About the Job

Requirements

A Bachelor's degree in Computer Science, Information Technology, or a related field, or equivalent experience.
At least 3 years of work experience in data analytics, data engineering, machine learning, or related fields.
Extensive experience writing software with Python.
Experience with multiple data architecture paradigms, such as MySQL, Microsoft SQL, Oracle, MongoDB, Kafka, Hadoop, HBase, and Spark.
Experience with infrastructure and continuous integration pipelines, such as Docker, Kubernetes, Airflow, and Jenkins.
Experience with open-source machine-learning libraries and frameworks, such as scikit-learn, TensorFlow, PyTorch, and Keras, and introducing accurate models into a production environment.
Ability to work under pressure while collaborating and managing competing demands with tight deadlines.

Responsibilities

Design and develop anomaly detection platforms for creating, tracking, and applying statistical and machine-learning models in production environments, focused on identifying deviations in real-time manufacturing processes using high-resolution and time-series data from global sites.
Create and refine machine-learning models to maximize accuracy and impact, incorporating techniques such as sequence modeling (including LSTMs and transformers) and feature engineering; engage stakeholders and rapidly iterate based on feedback to improve predictions for equipment failures and quality issues.
Design and develop ETLs, data pipelines, automation systems, and APIs for retrieving, processing, analyzing, and visualizing batch and real-time data, ensuring scalability and low-latency handling of large-scale datasets from worldwide equipment sources.
Analyze manufacturing, equipment, and vehicle data to extract useful statistics and insights about failures, enabling early prediction of issues and proactive actions through machine-learning-driven forecasting and pattern recognition across global sites.
Engage with cross-functional teams to identify underutilized data sources and develop new machine-learning methods to interact with and derive insights from them, such as advanced clustering or predictive analytics for site-specific optimizations.

Category	Manufacturing
Location	Fremont, California
Requisition ID	270071
Type	Full-time

Job Description

The Manufacturing Quality Data Engineering team specializes in predicting safety critical issues before they occur, protecting our customers. As part of Tesla's Vehicle Engineering organization, you'll have access to the data gold mine of design, manufacturing, and vehicle data sources, enabling you to design, develop, and deploy innovative generative SI and machine learning platforms globally. In this role, you'll focus on developing data pipelines for global machine learning platforms.

Job Responsibilities

Design and develop anomaly detection platforms for creating, tracking, and applying statistical and machine learning models in production environments, with a focus on identifying deviations in real-time manufacturing processes using high-resolution and time series data from global sites
Create and refine machine learning models to maximize accuracy and impact, incorporating techniques like sequence modeling (e.g., LSTMs, transformers) and feature engineering; engage stakeholders and rapidly iterate based on feedback to improve predictions for equipment failures and quality issues
Design and develop ETLs, data pipelines, automation systems, and APIs for retrieving, processing, analyzing, and visualizing batch and real-time data, ensuring scalability and low-latency handling of large-scale datasets from worldwide equipment sources
Analyze manufacturing, equipment, and vehicle data to extract useful statistics and insights about failures, enabling early prediction of issues and proactive actions through ML-driven forecasting and pattern recognition across global sites
Engage with cross-functional teams to identify underutilized data sources and invent new ML-based methods to interact with and derive insights from them, such as advanced clustering or predictive analytics for site-specific optimizations

Job Requirements

Bachelor's Degree in Computer Science, Information Technology, or a related field, or equivalent experience
3+ years of work experience in data analytics, data engineering, machine learning or related fields
Extensive experience writing software with Python
Experience with multiple data architecture paradigms (e.g. MySQL, MicrosoftSQL, Oracle, MongoDB, Kafka, Hadoop, Hbase, Spark)
Experience with infrastructure and continuous integration pipelines (e.g. Docker, Kubernetes, Airflow, Jenkins)
Experience with open source machine learning libraries and frameworks (e.g. Scikit-Learn, Tensorflow, PyTorch, Keras) and introduce accurate models to a production environment
Able to work under pressure while collaborating and managing competing demands with tight deadlines
A passion for machine learning and a results-oriented mindset

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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