LEAD AI 架构师(AIOS) LEAD AI Architect (AIOS)
Read straight from Mercedes-Benz Group AG's careers page, verified 1d ago.Seekless can tailor your résumé to it.
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Before you apply
LocationMultiple Locations
SeniorityLead
Experience5+ yrs
Company size10,000+ people
First seenOct 18, 2026 · 3w ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
5+ years of hands-on, production-oriented AI engineering experience
Strong track record in deploying and industrializing AI algorithms for automotive or embedded systems
Strong hands-on expertise in model training, fine-tuning, optimization, quantization, and deployment
Deep understanding of vehicle-cloud integrated AI systems
Knowledge of multiple vehicle domains (cockpit, ADAS/ADS, body, chassis, powertrain)
Solid understanding of vehicle E/E architecture and automotive software development process
Proven experience in on-vehicle integration, debugging, and problem-solving
Ability to independently deliver AI features from prototype to mass production
Excellent cross-team communication, stakeholder alignment, and execution skills
Outstanding analytical and problem-solving skills
Technical leadership and ability to make decisions based on technical facts
Strong sense of ownership and drive
Good communication skills and ability to work in a collaborative, cross-functional environment
English proficiency in written and spoken form
OEM or Tier-1 experience is highly preferred
Skills
AI
computer vision
large language models
cloud AI
on-board AI
model training
fine-tuning
optimization
quantization
deployment
Python
embedded systems
automotive SoCs
domain controllers
OTA
About the role
As a world-leading OEM, Mercedes-Benz seeks experts who are passionate and enthusiastic about applying AI technologies to the automotive industry. We are specifically looking for self-motivated and knowledgeable architect with a strong background in computer vision, large language models, and their practical applications to join our team.
As a Lead AI Architect, you will lead the design, development, and continuous evolution of an end-to-end, vehicle-cloud integrated AI architecture spanning major vehicle domains, including cockpit, ADAS, body, chassis, and powertrain. This role is highly hands-on, focusing on AI algorithm industrialization, use case deployment, model adaptation, and delivery to mass production. You will bridge cloud AI, vehicle-side embedded AI, and cross-domain software to build robust, deployable, and vehicle-qualified AI systems.
Task Description
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Own the end-to-end vehicle-cloud integrated AI architecture, covering the cloud AI platform, on-board AI deployment, cross-domain AI orchestration, closed-loop data, and model lifecycle management.
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Drive AI use case implementation end-to-end, from requirements definition and technical design through model selection, data preparation, training and fine-tuning, to vehicle-side optimization, integration, calibration, and mass-production release.
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Design and optimize cloud-side AI capabilities, including model evaluation, training data preparation, fleet-based model updates, and vehicle-cloud collaborative inference.
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Lead on-board AI model adaptation, including quantization, latency optimization, memory tuning, and power/thermal optimization for automotive SoCs and domain controllers.
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Translate research results, open-source models, and prototype algorithms into stable, reliable, automotive-grade AI functions that perform under real-world conditions.
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Collaborate closely with cloud platform, hardware, software and testing teams to align architecture, resolve blockers, and accelerate AI feature delivery.
Qualifications
•
5+ years of hands-on, production-oriented AI engineering experience, with a strong track record in deploying and industrializing AI algorithms for automotive or embedded systems. OEM or Tier-1 experience is highly preferred.
•
Strong hands-on expertise in model training, fine-tuning, optimization, quantization, and deployment.
•
Deep understanding of vehicle-cloud integrated AI systems, including cloud training pipelines, model management, on-board deployment, OTA model updates, and closed-loop data systems.
•
Knowledge of multiple vehicle domains, including cockpit, ADAS/ADS, body, chassis, and powertrain etc., with a solid understanding of vehicle E/E architecture and the automotive software development process.
•
Proven experience in on-vehicle integration, debugging, and problem-solving, with the ability to independently deliver AI features from prototype to mass production.
•
Excellent cross-team communication, stakeholder alignment, and execution skills, with the ability to drive complex AI architectures into real products.
•
Outstanding analytical and problem-solving skills.
•
Technical leadership and ability to make decisions based on technical facts.
•
Strong sense of ownership and drive.
•
Good communication skills and ability to work in a collaborative, cross-functional environment.
•
English proficiency in written and spoken form.