Software Engineer - Telematics

Applied Intuition
Sunnyvale, California, United States$183k – $253kPosted 26 March 2026

Job Description

About Applied Intuition Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co . We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. About the role As a Software Engineer - Telematics at Applied Intuition, you will be at the forefront of developing cutting-edge telematics platforms that enable seamless communication between devices, cloud services, and enterprise systems. You will work on sophisticated backend systems that combine telecommunications and informatics to process, analyze, and manage multi-modal real-time telemetry data. This role offers the opportunity to contribute to the rapidly growing cloud telematics market, building scalable data processing solutions that handle millions of connected devices and support enterprise-grade analytics platforms. At Applied Intuition, you will: Design and develop scalable robust telematics backend systems that process high-volume, real-time telemetry data streams from millions of connected devices using modern microservices architecture Architect and implement robust data processing pipelines using technologies like Apache Kafka, Apache Spark, and stream processing frameworks to handle telematics data ingestion, transformation, and analytics Develop cloud-based data storage and analytics solutions leveraging distributed databases, data warehouses, and time-series databases to support real-time monitoring and historical analysis Implement device-to-device (D2D) communication systems that enable direct data exchange between connected devices, reducing latency and improving network efficiency through peer-to-peer connectivity Collaborate with cross-functional teams including data scientists, DevOps engineers, product managers, and frontend developers to deliver comprehensive telematics solutions Work on advanced analytics and machine learning integration to derive actionable insights from telematics data for predictive maintenance, risk assessment, and behavioral analysis We're looking for someone who has: 4+ years of experience in backend software development with strong proficiency in languages like Go, C++, Python, and experience with cloud-native application development Experience with cloud platforms and services such as AWS, Azure, or Google Cloud Platform, including containerization with Docker and orchestration with Kubernetes Knowledge of data processing and streaming technologies including Apache Kafka, Apache Spark, MQTT, NATS, WebRTC, gRPC, and experience building high-throughput data pipelines Experience with database technologies including both SQL and NoSQL databases, data warehousing solutions, and time-series databases for handling large-scale telemetry data Understanding of API design and development with experience in RESTful services, microservices architecture, and API security best practices K ... (truncated, view full listing at source)
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