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Embedded AI Engineer – Android Automotive (on - device Intelligence)

Own on-device AI model lifecycle and optimization for Android Automotive end-to-end
Sunnyvale, California, United States
Junior
$150,000 – 250,000 USD / year
yesterday
Applied Intuition

Applied Intuition

Builds simulation, validation, and development tools that help engineers safely and efficiently develop, test, and deploy autonomous vehicle systems.

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Embedded AI Engineer – Android Automotive (On-Device Intelligence)

Sunnyvale, California, United States

About the Role

We are building on-device intelligence for a next-generation Android Automotive platform. This role owns the end-to-end lifecycle of embedded ML systems, ensuring models behave predictably and safely in production environments under real-world constraints such as latency, thermal limits, and functional safety.

At Applied Intuition, You Will:

  • Deploy and run production-grade ML inference and learning systems on Android Automotive (AAOS)
  • Implement on-device multimodal LLMs, including schema design and safe dispatch to local vehicle APIs
  • Integrate models using TensorFlow Lite, ONNX Runtime, or specialized vendor SDKs
  • Profile and optimize models for strict latency, memory, power, and thermal budgets
  • Instrument runtime performance across CPU, GPU, and NPU acceleration layers
  • Design safety boundaries and guardrails for model outputs, including tool-call allowlists and fallback logic
  • Interface directly with vehicle signals, sensors, and system services using C++ and JNI

We're Looking For Someone Who Has:

  • BS, MS, or PhD in Computer Science, Electrical Engineering, or a related technical field
  • 3+ years of experience shipping ML inference on embedded, mobile, or automotive platforms
  • Strong proficiency in C++ and experience with native Android integration (JNI)
  • Expertise in model optimization techniques such as quantization, pruning, and compilation
  • Experience integrating LLM function calling or tool execution with structured outputs
  • Hands-on experience with Android system services or Android Automotive OS (AAOS)
  • Deep understanding of edge constraints including real-time behavior and memory pressure

Compensation & Benefits

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.

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 a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position.

For pay transparency purposes, the base salary range for this full-time position in the location listed is: $150,000 - $250,000 USD annually.

Work Authorization

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 laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin.

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Embedded AI Engineer – Android Automotive (on - device Intelligence)
Sunnyvale, California, United States
$150,000 – 250,000 USD / year
Software
About Applied Intuition
Builds simulation, validation, and development tools that help engineers safely and efficiently develop, test, and deploy autonomous vehicle systems.