AI
Software Engineer
Applied IntuitionTokyo Prefecture, JapanPosted 22 April 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 Tokyo
Applied Intuition entered the Japan market in 2019. The company has rapidly grown and established strategic relationships with major Japanese automotive OEM customers. Based in Otemachi area, the Tokyo site consists of more than 60 employees who previously come from Woven by Toyota, Mobileye, TIER IV, Google, Siemens and other renowned companies. We are hiring our first new graduate class - want to learn more?
About the role
We are looking for a software engineer with a passion for improving developer experience and deployment processes. In this role, you will work across engineering teams to identify key sources of friction, improve workflows, develop new tools, and generally uplevel the velocity and reliability of the entire software, and automotive development and deployment lifecycle.
At Applied Intuition, you will:
Design, build, and improve the tools, libraries, and frameworks for our engineering team
Build and test scalable tools for deploying to various automotive and cloud platforms
Optimize our compile/build times using tools like Bazel, CMake, Docker
Improve software deployment and data collection frameworks
Identify and solve bottlenecks within our software stack
We're looking for someone who has:
5+ years of relevant experience
Fluency in both English and Japanese (written and verbal), with the ability to operate effectively in a bilingual business environment
Experience working directly with customers
Some experience using Python, Go, Java, or Kotlin
Experience with CI, build, and deployment technologies like Bazel, Buildkite, Docker, Kubernetes, and Jenkins, and comfortability with scripting for automation
Experience with Linux and/or ARM systems
Experience working with configuration management tools like Ansible and Terraform
Nice to have:
Experience using C and C++
Experience working in automotive or related industries
Experience with deploying software on either public clouds (e.g., AWS) or on-premise clusters
Benefits and perks:
Lunch and dinner allowance
Equity (RSU) offering
English / Japanese language allowance
Company sponsored housing program
Pension plan (確定拠出年金)
Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles.
Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these la ... (truncated, view full listing at source)
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