Staff Software Engineer – Vehicle AI

Jobtailor; California (MO); On-site; USD 170,000 - 210,000; Full time

Job summary

Jobtailor seeks a senior Android architect to lead the GM in-vehicle AI assistant platform on Android Automotive OS. You will drive technical roadmaps, code quality, and system design across engineering teams.

You will architect scalable interfaces and SDKs for downstream integration, implement hybrid edge-cloud processing, and establish automated testing and CI pipelines across vehicle builds. Collaboration with product and UX is essential.

Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, or equivalent practical experience.
• 8+ years of professional software development experience focused on production Android architecture.
• Proven technical leadership in architecture decisions and software execution across engineering teams.
• Expert proficiency in Kotlin and Java.
• Experience designing developer-facing APIs, platform SDKs, or shared application libraries.
• Experience establishing automated testing strategies and CI pipelines for mobile platforms.
• Experience with client-server architectures using streaming, gRPC, or REST protocols.
• Strong knowledge of Android components, lifecycle management, and IPC/AIDL.
• Master’s degree in Computer Science or Electrical Engineering is preferred.

Responsibilities

• Lead architectural design and execution of GM’s in-vehicle AI assistant application platform on Android Automotive OS.
• Drive engineering execution, technical roadmaps, code quality standards, and system design best practices.
• Architect scalable application interfaces and SDKs for downstream teams integrating specialized AI tools and vehicle capabilities.
• Implement hybrid edge-and-cloud architecture for low-latency on-device processing and cloud AI/ML services.
• Establish and scale automated testing, CI, and validation infrastructure across vehicle builds.
• Integrate model training and evaluation pipelines, system benchmarking, and quality feedback loops into platform deployment.
• Optimize voice and text response latency, memory footprint, and in-vehicle system responsiveness.
• Design application contracts using AIDL and IPC mechanisms.
• Collaborate with product management and user experience teams to translate customer interactions into platform capabilities.

Skills

Android Architecture

Kotlin

Java

Android Jetpack

Coroutines

Flow

Dependency Injection

AIDL

IPC Mechanisms

Client-Server Architectures

gRPC

REST Protocols

Voice Assistant Integration

Technical Leadership

Collaboration

Communication

Education

Bachelor’s degree in Computer Science / Computer Engineering

Master’s degree in CS or Electrical Engineering (Preferred)

Tools

Automated Validation Frameworks

Model Benchmarking

CI Tools

Job description

• Lead architectural design and execution of GM’s in-vehicle AI assistant application platform on Android Automotive OS


• Drive engineering execution, technical roadmaps, code quality standards, and system design best practices


• Architect scalable application interfaces and SDKs for downstream teams integrating specialized AI tools and vehicle capabilities


• Implement hybrid edge-and-cloud architecture for low-latency on-device processing and cloud AI/ML services


• Establish and scale automated testing, CI, and validation infrastructure across vehicle builds


• Integrate model training and evaluation pipelines, system benchmarking, and quality feedback loops into platform deployment


• Optimize voice and text response latency, memory footprint, and in-vehicle system responsiveness


• Design application contracts using AIDL and IPC mechanisms


• Collaborate with product management and user experience teams to translate customer interactions into platform capabilities

Requirements

• Bachelor’s degree in Computer Science, Computer Engineering, or equivalent practical experience


• 8+ years of professional software development experience, primarily focused on production Android application architecture


• Proven technical leadership in architecture decisions and software execution across engineering teams


• Expert proficiency in Kotlin and Java


• Expertise with Android Jetpack, Coroutines, Flow, and dependency injection frameworks such as Hilt or Dagger


• Experience designing developer-facing APIs, platform SDKs, or shared application libraries


• Experience establishing automated testing strategies and Continuous Integration (CI) pipelines for mobile software platforms


• Experience with client-server architectures using streaming, gRPC, or REST protocols


• Strong knowledge of Android application components, lifecycle management, and IPC/AIDL


• Master’s degree in Computer Science, Electrical Engineering, or related field is preferred


• Preferred experience with voice assistants, conversational frameworks, or agentic applications on mobile or automotive platforms


• Familiarity with Android Automotive OS (AAOS) applications is preferred


• Experience with client-side machine learning integration or on-device runtime optimization


• Experience with AI/ML evaluation pipelines, model benchmarking, and automated validation frameworks for conversational systems

Core Competencies

Demonstrates expertise in Android Automotive OS application architecture, with a strong focus on scalable design, automated testing, and integration of AI/ML services. Proven ability to lead technical teams and drive high-quality software execution while optimizing system performance.

Highest-signal resume keywords

• Android Application Architecture


• Kotlin and Java Proficiency


• Automated Testing and CI Pipelines


• AI/ML Integration and Optimization


• Technical Leadership in Software Execution

Hard Skills

• Android Jetpack


• Coroutines


• Flow


• Dependency Injection (Hilt, Dagger)


• AIDL


• IPC Mechanisms


• Client-Server Architectures


• GRPC


• REST Protocols


• Voice Assistant Integration

Soft Skills

• Collaboration


• Technical Leadership


• Communication

Certifications & Qualifications

• Bachelor's Degree in Computer Science


• Master's Degree in Computer Science or Electrical Engineering (Preferred)

Industry Keywords

• In-Vehicle AI Assistant


• Automotive Platforms


• Conversational Frameworks


• Mobile Software Platforms

Tools & Technologies

• Automated Validation Frameworks


• Model Benchmarking


• Continuous Integration Tools