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

InvestedintheMission; Sunnyvale (CA); Hybrid; USD 190,000 - 260,000; Full time

Benefits offered by this job

Equity

Health insurance

401k match

Paid time off

Job summary

Applied Intuition, Inc. is 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 under latency, memory, and safety constraints. You will deploy production ML on AAOS, implement multimodal LLMs, and optimize for real-time edge environments, interfacing with vehicle signals via C++ and JNI.

Qualifications

• 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.

Responsibilities

• Deploy 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.

Skills

C++

JNI

Embedded ML

Android AAOS

Model optimization

LLM integration

Real-time systems

Edge computing

Education

BS/MS/PhD in CS/EE or related field

Tools

TensorFlow Lite

ONNX Runtime

Job description

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 full-time 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. This in-office expectation does not apply to contractor positions

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

Nice to have:

• Experience with Snapdragon Automotive, ARM Ethos, or specialized NPU pipelines

• Background in running quantized LLMs on‑device using llama.cpp or TFLite transformers

• Familiarity with functional safety concepts (ISO 26262), sandboxing, or policy enforcement

• Experience bridging cloud‑trained models to resource‑constrained embedded runtimes

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.

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. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws.

FOR US-BASED ROLES:

Applied Intuition is committed to providing an accessible and inclusive application and interview experience to applicants who are disabled veterans and other applicants with disabilities or medical conditions. Reasonable accommodations are available, requesting an accommodation will not affect your candidacy in any way, and you are not required to disclose the nature of your disability or medical condition in order to make a request. If you require an accommodation please contact [e-mail]. We will work with you!