Cloud Backend Software Engineer (AI Applications)

The Company

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 5 million vehicles on the road today and is 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.

The Opportunity

We are seeking a highly motivated Cloud Backend Software Engineer for AI Applications to join our team and help us accelerate software innovations for next-generation software-defined vehicles. In this role, you will be responsible for designing and implementing large-scale data processing pipelines and building the cloud infrastructure to support our AI applications. You’ll use your deep expertise in cloud architecture and backend development to make critical decisions that span data processing, API design, and cloud service deployment. This is a key leadership position for a hands-on engineer who will define our data governance, infrastructure as code, and production observability best practices.

Salary range: $102,000 – $153,000 per year

Requirements

• BS degree (MS or PhD preferred) in Computer Science, Math, Physics, or related fields
• 5+ years of experience building and scaling data-intensive, cloud-based services and applications.
• Deep expertise in deploying and managing cloud-native workloads on various cloud environments.
• A “systems thinker” with a demonstrated ability to architect end-to-end solutions, possessing a strong foundation in both Data Engineering and Cloud/Backend Software Development.
• Deep proficiency in Golang or Python.
• Strong programming fundamentals, particularly in data structures & algorithms, and concurrency.
• Hands-on experience with containerization/orchestration (Docker, Kubernetes).
• Experience building and optimizing highly available, scalable, distributed backend services leveraging open source technologies and tools such as Kafka, RabbitMQ, and ElasticSearch.
• Ability to deeply analyze, break down, and solve complex problems.
• Good verbal and written communication skills, with the ability to explain complex ideas and solutions clearly, and to collaborate with cross-functional teams effectively.

Nice to have

• Familiarity with Infrastructure as Code (IaC) tools (e.g., Terraform, Pulumi) to support cloud environment setup and service deployment
• Experience with developing data pipelines and database-supported cloud services and applications

Role and Responsibilities

• Design and build the foundational, end-to-end cloud and backend platform for our Generative AI systems. You’ll make critical decisions that span data processing, API design, and service deployment.
• Build and scale the data pipelines that are the lifeblood of our applications. This includes processing massive datasets and creating the real-time data flows for our AI systems. You will design and implement large-scale data processing pipelines that handle a diverse set of data collected from millions of vehicles.
• Deploy, scale, and optimize our cloud infrastructure. You will manage hardware resources, minimize inference latency, and build robust monitoring to ensure our AI system is always fast, accurate, and cost-effective. This includes developing and deploying containerized applications using Docker and Kubernetes.
• Create a single, cohesive set of best practices for the entire application lifecycle. Your work will define how we handle data governance, infrastructure as code, and production observability in one seamless system.
• Automate infrastructure deployment using Infrastructure as Code (Terraform, Pulumi, etc.), ensuring repeatability across cloud environments.
• Deliver production-ready CI/CD pipelines for automated provisioning and deployment of containerized workloads.