Software Engineer - Kafka
Applied IntuitionSunnyvale, California, United States$153k – $222kPosted 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 the data engine team, our goal is to provide a central data and machine learning platform that can be used across all verticals of the company. We’re looking for generalist engineers that want to build the foundations of a new workflow that spans:
Data ingestion from a production fleet
Data processing and storage (TBs/car/day)
Labeling infrastructure
Machine learning infrastructure
This group has a massive scope to define how product verticals across the company are deployed at scale for our customers given data and machine learning infrastructure is at the heart of the autonomy problem. We’re still very early in development so if you are interested in the “0-1” stage of building up a new team that interacts with teams across the company, this is a good fit.
At Applied Intuition, you will:
Design and build large-scale data platforms to support our AI research and autonomy stack development, handling petabytes of multimodal sensor data from real-world driving scenarios
Work on data curation and tagging platforms that enable efficient dataset discovery, labeling workflows, and quality assessment across diverse driving conditions
Build high-performance data processing systems using modern distributed computing frameworks to transform raw sensor data into training-ready formats
Use the following technologies: Apache Spark, Apache Hudi, Trino, Apache Kafka, Flyte, Kubernetes, Python, Golang, Java
We're looking for someone who has:
A Bachelor's degree in Computer Science, Software Engineering, or equivalent
2+ years of professional experience
Strong backend engineering experience
Problem-solving skills and experience working with cross-functional teams in fast-paced environments
Nice to have:
Hands-on experience with modern data stack technologies including Apache Spark, Hudi, Trino, Kafka, or similar distributed data processing frameworks
Knowledge of data lake architectures, streaming systems, and workflow orchestration platforms like Flyte
Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment.
Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to ... (truncated, view full listing at source)
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