Embedded AI Engineer – Android Automotive (On-Device Intelligence)
Applied Intuition is hiring an Embedded AI Engineer – Android Automotive (On-Device Intelligence) in Sunnyvale, United States. It pays $183k-$253k a year and Level rates it ; you can apply on Applied Intuition.
Level's read on this job
- Builds AI ●●●● is how Level rates this AI engineer role: the job is building AI systems.
- The listed pay starts close to the typical AI engineer posting on Level.
- Tools and skills the posting cites include Llama and TensorFlow.
- Level classifies it as a mid-level role.
- Level lists 327 other open roles at Applied Intuition.
AI in this role
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 roleWe 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
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
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.
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 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 careers@applied.co. We will work with you!
How we rate this
Embedded AI Engineer – Android Automotive (On-Device Intelligence) at Applied Intuition rates 87 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in AI engineer jobs at this level
Pay
- This job starts at
- $183k
- Typical AI engineer job
- $185k
This posting starts close to the typical AI engineer job. See AI engineer jobs
Questions you could be asked
- What's a project where you used Llama hands-on?
- Walk me through how you've used TensorFlow in your day-to-day work.
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: Llama and TensorFlow. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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