Tesla

Makes electric cars, robotaxis and grid batteries

Process Engineer - Incoming Quality

Quality EngineeringConfirmed live in the last 24 hours

Full-timeFremont, CA, USANo salary listed

About the Job

Requirements

A bachelor's degree in Mechanical Engineering, Electrical Engineering, Industrial Engineering, Computer Science, or a related technical field, or equivalent experience.
Experience in automotive or robotics assembly, manufacturing operations, or hands-on vehicle repair, with a strong understanding of a production environment.
Hands-on experience using artificial intelligence technology and developing bots or automation for data analytics in a manufacturing, quality, or operations setting, including Python, SQL, scheduled jobs, or AI-assisted analysis.
Ability to build repeatable analytics, such as automated scrap, Material Review Board, or quality reports, that replace one-off spreadsheet work and remain accurate as processes change.
Hands-on proficiency with electromechanical systems, tools, instrumentation, and measurement techniques in an industrial or manufacturing setting.
Knowledge of manufacturing processes, quality methods, and structured problem-solving such as 8D and root cause analysis, supported by experience collecting, analyzing, and interpreting data.
Strong analytical and communication skills, proficiency in Excel, SQL, Python, or similar tools, and a track record of delivering results under pressure in a fast-paced environment.

Responsibilities

Drive continuous improvement in SRT, Material Review Board, and Scrap processes across Model 3, Model Y, Optimus, and EOL departments.
Use artificial intelligence technology to analyze scrap, Material Review Board backlog, incoming quality, and throughput data, and surface patterns, drivers, and actions faster than manual review.
Develop, deploy, and maintain bots and automation for data analytics, including scheduled data pulls, exception reports, and recurring briefs, so analysis runs without a manual export.
Collaborate with Production, Quality Engineering, Supplier Quality, Production Control, Packaging, Warehouse, and Material Review Board teams to define, qualify, and track projects that reduce scrap and improve process reliability.
Identify optimization opportunities across Fremont workflows to improve operational efficiency and reduce scrap-related cost.
Apply lean methods, including value stream mapping, line balancing, 5S, material handling, and safety improvements, to remove waste and sustain higher yield.
Lead process improvements across vehicle assembly and track progress, timelines, and deliverables with standard project tools so work is completed on time.
Provide technical leadership and operational support to improve manufacturing processes, with emphasis on Scrap, Material Review Board, and Incoming Quality processes.
Lead initiatives to reduce scrap rates, enhance yield, and streamline Material Review Board workflows through data-driven analysis, artificial-intelligence-enabled analytics, and process optimization.
Develop analytics and automation that pull, analyze, and report operational data so leaders can act on scrap, Material Review Board, and incoming-quality issues without manual exports.
Spearhead implementation of OneMRB within Optimus Operations to standardize and digitize material review activities.
Coordinate cross-functionally with Engineering, Quality, Supply Chain, and Production teams to resolve process challenges, implement corrective actions, and ensure seamless execution of TSORT and rework processes.
Drive accountability, transparency, and continuous improvement across critical quality and production systems.

Category	Manufacturing
Location	Fremont, California
Requisition ID	287566
Type	Full-time

Job Description

Provide technical leadership and operational support to drive continuous improvement of manufacturing processes at the Fremont facility, with a focused emphasis on Scrap, Material Review Board (MRB), and Incoming Quality processes. Lead initiatives to reduce scrap rates, enhance yield, and streamline MRB workflows through data-driven analysis, AI-enabled analytics, and process optimization. Utilize AI technology and develop bots and automation that pull, analyze, and report operational data so leaders can act on scrap, MRB, and incoming-quality issues without manual exports. Spearhead the implementation of OneMRB within Optimus Operations to standardize and digitize material review activities. Coordinate cross-functionally with Engineering, Quality, Supply Chain, and Production teams to resolve process challenges, implement corrective actions, and ensure seamless execution of TSORT and rework processes. Act as a key enabler of operational excellence by driving accountability, transparency, and continuous improvement across critical quality and production systems. 1st Shift: 07:30AM - 04:30PM; Monday - Friday

Job Responsibilities

Drive continuous improvement in SRT, MRB, and Scrap processes across Model 3, Model Y, Optimus, and EOL departments
Utilize AI technology to analyze scrap, MRB backlog, incoming quality, and throughput data, and to surface patterns, drivers, and actions faster than manual review
Develop, deploy, and maintain bots and automation for data analytics, including scheduled data pulls, exception reports, and recurring briefs, so analysis runs without a manual export
Collaborate with Production, Quality Engineering, Supplier Quality, Production Control, Packaging, Warehouse, and MRB to define, qualify, and track projects that reduce scrap and improve process reliability
Identify optimization opportunities across Fremont workflows to improve operational efficiency and reduce scrap-related cost
Apply lean methods, including value stream mapping, line balancing, 5S, material handling, and safety improvements, to remove waste and sustain higher yield
Lead process improvements across vehicle assembly and track progress, timelines, and deliverables with standard project tools so work is completed on time

Job Requirements

Bachelor's Degree in Mechanical Engineering, Electrical Engineering, Industrial Engineering, Computer Science, or a related technical field, or equivalent experience
Proven experience in automotive or robotics assembly, manufacturing operations, or hands-on vehicle repair, with a strong understanding of a production environment
Hands-on experience utilizing AI technology and developing bots or automation for data analytics in a manufacturing, quality, or operations setting, including Python, SQL, scheduled jobs, or AI-assisted analysis
Demonstrated ability to build repeatable analytics, such as automated scrap, MRB, or quality reports, that replace one-off spreadsheet work and stay accurate as the process changes
Hands-on proficiency with electromechanical systems, tools, instrumentation, and measurement techniques in an industrial or manufacturing setting
Solid knowledge of manufacturing processes, quality methods, and structured problem-solving such as 8D and root cause analysis, supported by experience collecting, analyzing, and interpreting data
Strong analytical and communication skills, with proficiency in Excel, SQL, Python, or similar tools, and a track record of delivering results under pressure in a fast-paced environment

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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