AI Engineer Automotive Software Innovation (Sunnyvale, CA | Hybrid | $225K to $300K)
About the Company
A rapidly growing automotive technology company is revolutionizing the evolution of vehicles through software. Their AI-driven platform enables next-generation, software-defined vehicles, bridging the gap between automotive reliability and consumer-grade innovation. With technology already deployed in over 5 million vehicles worldwide, they are trusted by top global OEMs to power the future of connected mobility.
Role Overview
We are seeking a highly skilled AI Engineer with deep expertise in machine learning, data science, and model optimization. This is a hands-on role where you'll help design, implement, and deploy scalable AI models that enhance intelligent vehicle systems. You'll collaborate closely with cross-functional teams across data, embedded systems, and cloud infrastructure to deliver production-ready AI solutions.
Key Responsibilities
Design and develop AI/ML models using Python, TensorFlow, PyTorch, and ONNX.
Optimize model performance and deploy solutions across distributed automotive environments.
Collaborate with data and engineering teams to operationalize ML pipelines using MLOps best practices.
Conduct experiments, evaluate models, and iterate to ensure reliability, efficiency, and scalability.
Present solutions and technical insights to leadership and technical peers.
Requirements
Bachelors or Masters degree in Computer Science, AI/ML, or related field.
4+ years of experience in applied machine learning or AI engineering.
Strong proficiency in Python and experience with TensorFlow, PyTorch, or similar frameworks.
Solid understanding of data pipelines, MLOps, and model deployment.
Experience with real-time, embedded, or distributed systems is a strong plus.
Excellent communication skills and a collaborative, problem-solving mindset.
Location & Work Setup
Hybrid Sunnyvale, CA
Compensation
$225,000 $300,000 + competitive benefits
Interview Process
Recruiter Screening
Technical Interview (Python Coding)
Onsite Technical Presentation (Algorithms, ML, Data Science)
Final Interview with CTO
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