Senior Staff Customer Engineer – Automotive AI

Sonatus•Sunnyvale, CA

9h•Hybrid

About The Position

At Sonatus, we’re driving the transformation to AI-enabled software-defined vehicles. Traditional automotive software methods can’t keep pace with consumer expectations shaped by the mobile industry—where features evolve rapidly, update seamlessly, and improve continuously. That’s why leading OEMs trust Sonatus to accelerate this shift. Our technology is already in production across more than 6 million vehicles on the road today and rapidly expanding. Headquartered in Sunnyvale, CA, with 250+ employees worldwide, Sonatus combines the agility of a fast-growing company with the scale and impact of an established partner. Backed by strong funding and proven by global deployment, we’re solving some of the most interesting and complex challenges in the industry. Join us and help redefine what’s possible as we shape the future of mobility. Sonatus is seeking a highly motivated Senior/Staff AI Engineer to join our Customer Engineering (CE) team and help deliver AI-driven solutions for automotive OEM and Tier-1 customers. In this role, you will work directly with customers to understand real-world challenges in their vehicle environments and translate those challenges into practical solutions using Sonatus AI technologies. A key responsibility of this role is to integrate and deploy AI capabilities on top of the Sonatus AI platform within cloud environments (Sonatus or customer-managed) while ensuring seamless interaction with vehicle-side systems and data. This position operates in a forward-deployed engineering model, working closely with customer engineering teams to deliver end-to-end solutions from problem definition to deployment.

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field (Master’s or PhD preferred)
12+ years of experience in AI / Machine Learning, Data Engineering, or related domains, with exposure to automotive software or vehicle systems
Solid understanding of machine learning, data analytics, and AI-based systems, including practical experience delivering real-world solutions
Experience designing and working with data pipelines, analytics workflows, and model-driven systems
Strong experience deploying and operating software in cloud environments (AWS, Azure, GCP, or customer-managed infrastructure)
Experience integrating platform-based solutions with external systems, data pipelines, and APIs
Familiarity with containerization and deployment technologies such as Docker and Kubernetes
Strong hands-on experience developing, integrating, and deploying AI/ML-based solutions in production environments
Proven ability to influence customer direction and drive technical decision-making in customer-facing engagements
Experience leading technical discovery, shaping ambiguous problem spaces, and proposing scalable solution approaches
Proven ability to communicate complex technical concepts clearly and persuasively to both technical and non-technical stakeholders
Ability to operate effectively in ambiguous environments and drive clarity, alignment, and execution
Experience in customer engineering, solution engineering, or forward-deployed engineering roles is highly preferred

Nice To Haves

Familiarity with modern AI methodologies such as Agentic AI, Retrieval-Augmented Generation (RAG), or data-driven automation is a plus

Responsibilities

Partner with automotive OEMs and Tier 1 suppliers to identify, shape, and prioritize high-impact AI-driven use cases, guiding customers toward scalable and high-leverage solutions
Act as a trusted advisor to customers by shaping problem definition, challenging assumptions, and guiding technical decision-making toward scalable AI solutions
Drive alignment and adoption of solutions by clearly articulating trade-offs, ROI, and long-term architectural implications to technical and executive stakeholders
Design, integrate, and deploy AI/ML-based solutions across cloud and in-vehicle environments, enabling end-to-end vehicle-to-cloud use cases with reliable data flow and system interoperability
Develop and iterate AI models, PoCs, and production-ready solutions, continuously improving performance based on real-world data and customer feedback
Lead technical discussions and solution reviews focused on AI feasibility, model performance, trade-offs, and system limitations
Identify risks related to AI deployment and integration (e.g., data availability, security constraints, model reliability) and drive mitigation strategies
Collaborate with internal teams (AI Platform, Product, Engineering) to enhance AI capabilities, model deployment pipelines, and platform scalability