Software Engineer (On-Device AI Prototyping) (Senior/Staff level)

📍 Job Overview

Job Title: Software Engineer (On-Device AI Prototyping) (Senior/Staff level)

Company: Qualcomm Vietnam Company Limited, Hanoi Branch Office

Location: Hanoi, Vietnam / Ho Chi Minh City, Vietnam

Job Type: Full-Time

Category: Machine Learning Engineering / Software Engineering

Date Posted: April 17, 2026

Experience Level: Senior/Staff (2-5+ years)

Remote Status: On-site

🚀 Role Summary

• Develop cutting-edge on-device AI prototypes leveraging generative AI, computer vision, and agentic workflows on Snapdragon SoCs.

• Build high-performance software for mobile, automotive, XR, robotics, and IoT applications, focusing on real-time execution.

• Collaborate closely with AI researchers to translate advanced AI models into functional, on-device prototypes and demos.

• Optimize software for performance, responsiveness, memory usage, and device constraints within embedded and Android environments.

📝 Enhancement Note: This role is positioned within Qualcomm's AI Research division, emphasizing the development of early-stage AI capabilities on embedded hardware. The "Senior/Staff" designation implies a need for strong technical leadership, problem-solving skills, and the ability to influence technical direction in prototyping efforts. The focus on "prototyping" suggests a dynamic environment where rapid iteration and creative problem-solving are paramount, bridging the gap between theoretical AI research and practical, deployable solutions.

📈 Primary Responsibilities

• Design, develop, and implement prototype applications and system components that enable advanced AI features to run efficiently on-device.

• Integrate AI models into Android or embedded applications using established runtimes and internal Qualcomm tools, working alongside AI researchers.

• Build robust, clean, and maintainable software systems that facilitate rapid prototyping cycles and internal demonstration purposes.

• Develop and contribute to documentation, internal tools, utilities, and frameworks to enhance engineering workflows and knowledge sharing.

• Investigate and resolve complex performance bottlenecks related to responsiveness, memory footprint, thermal constraints, and power consumption on target hardware.

• Apply best practices in software development, including code quality standards, effective version control strategies, automated testing methodologies, and maintainable design principles.

• Collaborate with Systems/AI engineers to establish seamless pre- and post-processing pipelines, model interfaces, and data flows essential for on-device AI execution.

• Provide practical feedback to research teams regarding the feasibility of AI features, potential performance limitations, and the impact on user experience.

• (Preferred) Develop internal tools, automation scripts, and lightweight backend services to streamline data flow, testing procedures, or demo deployment.

• Assist multidisciplinary teams by sharing technical insights, providing debugging support, and contributing to API and architecture documentation.

• Maintain clear and concise written technical materials, including design summaries, architectural diagrams, and workflow notes, to accelerate team progress and understanding.

📝 Enhancement Note: The responsibilities highlight a blend of core software engineering (C++, Android, embedded systems) with specialized AI integration and performance optimization. The emphasis on "prototyping" and "demos" indicates a fast-paced, iterative development cycle where delivering functional proof-of-concepts is key. The "Preferred" tooling and infrastructure responsibilities suggest opportunities for candidates with a broader skillset in automation and DevOps to contribute significantly.

🎓 Skills & Qualifications

Education:

• Bachelor’s degree in Computer Science, Software Engineering, Electrical Engineering, Information Systems, or a closely related technical field.

Experience:

• Minimum of 4+ years of professional software development experience for those with a Bachelor's degree.

• Minimum of 3+ years for those with a Master's degree.

• Minimum of 2+ years for those with a PhD.

Required Skills:

• Programming Proficiency: Strong command of C++ and/or Java/Kotlin for Android development, or embedded C/C++.

• Platform Experience: Hands-on experience with Android development (SDK/NDK) or embedded/Linux-based systems.

• Software Fundamentals: Solid understanding of core software engineering principles, including memory management, multi-threading, concurrency, and system behavior on resource-constrained devices.

• Performance Optimization: Demonstrated experience in optimizing applications for responsiveness, efficiency, and minimal resource utilization.

• Debugging Skills: Ability to effectively debug complex issues across various layers of the software stack, from application to system level.

• System Design: Understanding of software architecture and design principles for building maintainable and scalable systems.

Preferred Skills:

• On-Device AI Experience: Prior experience in bringing AI capabilities (e.g., machine learning models, computer vision algorithms) onto mobile or embedded devices.

• Qualcomm Chipset Familiarity: Experience working with Qualcomm chipsets (Snapdragon SoCs) is a significant advantage, though not strictly required.

• Hardware Constraint Awareness: Familiarity with mobile/embedded hardware limitations such as performance ceilings, thermal envelopes, and power consumption budgets.

• Tooling & Automation: Experience building internal development tools, automation scripts, or lightweight backend services to support development workflows.

• Large Codebase Navigation: Comfort and proficiency in navigating and contributing to large, complex codebases, build systems, and cross-platform development environments.

• AI Frameworks: Familiarity with common AI/ML frameworks and runtimes (e.g., TensorFlow Lite, PyTorch Mobile, ONNX Runtime) for on-device deployment.

📝 Enhancement Note: The minimum qualifications clearly outline the expected technical depth. The "Preferred Qualifications" section is crucial for candidates looking to stand out. Highlighting experience with Qualcomm hardware or specific AI frameworks used in embedded scenarios will be highly beneficial. The emphasis on both C++ and Android development suggests a need for versatility.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrated AI Prototyping: Showcase examples of AI models or algorithms successfully implemented and run on mobile or embedded devices. This could include demos of computer vision tasks, natural language processing, or generative AI applications.

• Performance Optimization Case Studies: Provide detailed examples of how you identified and resolved performance bottlenecks in software, specifically highlighting improvements in areas like latency, throughput, memory usage, or power consumption. Quantifiable results are essential.

• System Integration Examples: Include projects where you integrated different software components or systems, particularly those involving mobile applications, embedded systems, or backend services that interact with on-device functionalities.

• Code Quality & Maintainability: Present code samples or project descriptions that emphasize clean coding practices, modular design, adherence to coding standards, and effective use of version control (e.g., Git).

Process Documentation:

• Workflow Design & Optimization: Document projects where you designed or optimized software development workflows, emphasizing efficiency gains, reduced cycle times, or improved collaboration.

• Implementation & Automation: Showcase instances where you implemented new features, systems, or automated processes, detailing the challenges faced and the solutions implemented.

• Measurement & Analysis: Demonstrate your ability to define key performance indicators (KPIs) for software projects, collect relevant data, and analyze results to drive improvements or validate success.

📝 Enhancement Note: For a role focused on AI prototyping and performance on devices, a portfolio demonstrating practical application of AI, strong C++/Android development skills, and a clear ability to optimize for resource-constrained environments will be critical. Candidates should be prepared to walk through their portfolio, explaining the technical challenges, their solutions, and the measurable impact of their work, particularly concerning performance metrics.

💵 Compensation & Benefits

Salary Range:

As this role is located in Vietnam, specific salary ranges can vary significantly based on experience level (Senior/Staff), specific skills, and the prevailing market rates in Hanoi and Ho Chi Minh City. Based on industry benchmarks for Senior/Staff Software Engineers in technology hubs within Southeast Asia, a competitive annual salary range for this position would likely fall between VND 800,000,000 to VND 1,500,000,000+ (approximately USD $32,000 to $60,000+).

Benefits:

• Comprehensive Health Insurance: Coverage for employees and potentially dependents, including medical, dental, and vision.

• Retirement/Provident Fund Contributions: Employer contributions to a local retirement savings plan.

• Paid Time Off (PTO): Generous annual leave, sick leave, and public holidays as per Vietnamese labor law, potentially enhanced by company policy.

• Performance Bonuses: Potential for annual bonuses based on individual and company performance.

• Stock Options/Equity: Possible eligibility for Qualcomm's stock purchase plans or equity grants, especially for senior roles.

• Professional Development: Opportunities for training, certifications, attending industry conferences, and access to online learning platforms.

• Relocation Assistance: If applicable, support for relocation to Vietnam.

• Employee Assistance Programs (EAP): Confidential counseling and support services.

• On-site Amenities: Depending on the office location, potential for subsidized meals, fitness facilities, or other campus benefits.

Working Hours:

• Standard full-time work week, typically 40 hours per week.

• While core hours exist, Qualcomm often promotes flexibility, allowing for some adjustment in start and end times, provided team collaboration and project deadlines are met. The nature of prototyping may also require occasional flexibility to meet critical demo or integration deadlines.

📝 Enhancement Note: Salary estimates are based on general market data for senior software engineering roles in Vietnam's major tech cities and Qualcomm's typical compensation structure for experienced engineers. Actual compensation will be determined by the hiring team based on the candidate's specific qualifications, interview performance, and internal salary bands. Benefits are typical for large multinational tech companies operating in Vietnam.

🎯 Team & Company Context

🏢 Company Culture

Industry: Semiconductor / Technology (Artificial Intelligence, Mobile Computing, IoT)

Company Size: Qualcomm is a large, multinational corporation with tens of thousands of employees globally. This specific role is within the Qualcomm AI Research Vietnam division, which, while part of a large entity, likely operates with a more focused, research-oriented culture.

Founded: Qualcomm was founded in 1985, bringing decades of innovation and leadership in mobile technology. The AI Research division is a more recent, but rapidly growing, strategic focus area.

Team Structure:

• Qualcomm AI Research Vietnam: This team is likely composed of highly skilled engineers and researchers specializing in various AI domains (ML, CV, Generative AI) and software engineering disciplines (embedded systems, Android development, performance optimization).

• Reporting Structure: Senior/Staff Engineers typically report to an Engineering Manager or Director within the AI Research group. They are expected to operate with a high degree of autonomy, mentor junior engineers, and contribute to technical strategy.

• Cross-Functional Collaboration: This role will involve extensive collaboration with AI researchers to translate models into prototypes, with systems engineers for hardware integration, and potentially with product teams to understand future application needs. Collaboration extends globally across Qualcomm's research and engineering hubs.

Methodology:

• Data-Driven Prototyping: The team likely employs a data-driven approach to validate AI models and optimize software performance on target hardware. Metrics and empirical results are paramount.

• Agile/Iterative Development: Given the prototyping nature, expect an agile or iterative development methodology focused on rapid cycles of development, testing, and feedback.

• Performance-Centric Practices: A strong emphasis on performance analysis, profiling, and optimization techniques is inherent to working with on-device AI and embedded systems.

Company Website: https://www.qualcomm.com/

📝 Enhancement Note: Qualcomm's culture is generally characterized by innovation, technical excellence, and a fast-paced environment. For the AI Research division, expect a strong emphasis on cutting-edge technology, collaboration with world-class researchers, and a focus on pushing the boundaries of what's possible on mobile and embedded platforms. The "Senior/Staff" level suggests an expectation of leadership and mentorship.

📈 Career & Growth Analysis

Operations Career Level: This role represents a Senior to Staff Software Engineer position within the AI Research domain. At this level, individuals are expected to:

• Possess deep technical expertise in their specialization (e.g., C++, Android, embedded AI).

• Lead complex technical projects from conception to delivery of functional prototypes.

• Mentor and guide junior engineers, fostering their technical development.

• Influence technical decisions and contribute to the architectural direction of prototyping efforts.

• Act as a subject matter expert in specific areas of on-device AI software development.

Reporting Structure: Typically, Senior/Staff Engineers report to a Manager or Director of Engineering within the AI Research group. They often work within project teams that may include AI Researchers, other Software Engineers, and Systems Engineers.

Operations Impact: The impact of this role is significant in bridging the gap between theoretical AI research and tangible product capabilities. Success means enabling Qualcomm to demonstrate and validate advanced AI features on its hardware platforms, influencing future product roadmaps and solidifying Qualcomm's position as a leader in intelligent edge computing. This role directly contributes to Qualcomm's competitive advantage in AI inference on its SoCs.

Growth Opportunities:

• Technical Specialization: Deepen expertise in specific AI domains (e.g., generative models, computer vision) or system-level optimizations for AI hardware.

• Leadership Development: Progress into Lead Engineer or Technical Manager roles, overseeing larger projects or teams.

• Cross-Functional Mobility: Opportunities to move into roles focusing on product integration, AI architecture, or research strategy within Qualcomm.

• Industry Recognition: Contribute to patents, publications, and industry standards related to on-device AI.

• Global Exposure: Work with diverse teams across Qualcomm's international research and development centers.

📝 Enhancement Note: The "Senior/Staff" designation is key. Candidates should highlight leadership, mentorship, and significant technical contributions in their experience. The growth path clearly points towards increasing technical leadership and strategic influence within Qualcomm's AI initiatives.

🌐 Work Environment

Office Type: Qualcomm typically operates modern, well-equipped office spaces designed to foster collaboration and innovation. This is an on-site role, emphasizing the importance of in-person teamwork and access to development hardware.

Office Location(s): The role is based in either Hanoi or Ho Chi Minh City, Vietnam. These locations are strategic hubs for Qualcomm's engineering talent in the region.

Workspace Context:

• Collaborative Spaces: Offices are likely to include open work areas, meeting rooms, and dedicated spaces for team collaboration and brainstorming.

• Development Hardware: Access to cutting-edge development hardware, including Snapdragon development kits and various mobile/embedded devices, will be readily available for prototyping and testing.

• Team Interaction: Opportunities for regular interaction with AI researchers, fellow software engineers, and systems engineers to facilitate knowledge sharing and problem-solving.

Work Schedule:

• The standard work schedule is typically 40 hours per week.

• While a structured work schedule is in place, the nature of prototyping and working with cutting-edge technology may occasionally require flexibility to meet project deadlines, especially for critical demos or integration milestones. This flexibility is usually balanced with a culture that respects work-life balance.

📝 Enhancement Note: The on-site requirement is critical. Candidates should be prepared for a collaborative office environment where direct interaction with team members and access to specialized hardware are integral to the role. The presence in Vietnam suggests a dynamic regional tech hub environment.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter will typically conduct an initial call to assess basic qualifications, interest, and fit.

• Technical Phone Screen: A more in-depth discussion with an engineer or hiring manager focusing on core technical skills (C++, Android, systems programming, AI concepts).

Expect problem-solving questions and scenario-based inquiries.

• On-Site/Virtual Interviews (Multiple Rounds): This will involve several sessions with different team members, including:
  • Deep Dive Technical Interviews: Focused on algorithm design, data structures, system design, performance optimization, and on-device AI concepts.
  • Portfolio Review: A dedicated session where you will present selected projects from your portfolio, explaining your contributions, technical challenges, solutions, and the impact of your work. Be prepared to discuss performance metrics and optimization strategies in detail.
  • Behavioral/Situational Interviews: Assessing your problem-solving approach, teamwork, communication skills, and how you handle challenges. Questions may relate to collaboration with researchers or handling ambiguity in prototyping.
  • Manager/Director Interview: A final discussion to assess overall fit, career aspirations, and alignment with the team's goals.

Portfolio Review Tips:

• Select Relevant Projects: Choose 2-3 projects that best showcase your experience in C++, Android development, embedded systems, AI integration, and performance optimization.

• Focus on Impact: Clearly articulate the problem you solved, your specific contributions, the technical approach you took, and the measurable results (e.g., performance improvements, efficiency gains). Use quantifiable data whenever possible.

• Structure Your Presentation: For each project, follow a clear narrative: problem statement, your role/contribution, technical details, challenges faced, solutions implemented, and outcomes/impact.

• Be Ready for Deep Dives: Anticipate detailed questions about your code, design choices, and debugging process. Be prepared to discuss trade-offs you made.

• Demonstrate AI Understanding: Even if your projects aren't strictly AI, be ready to discuss how your software engineering skills enable AI functionality or how you would approach optimizing AI workloads.

Challenge Preparation:

• System Design: Practice designing scalable and efficient systems, considering resource constraints relevant to embedded devices.

• Algorithm & Data Structures: Brush up on common algorithms and data structures, with a focus on efficiency (time and space complexity).

• C++/Android/Embedded Concepts: Review key concepts related to memory management, threading, concurrency, IPC, and Android framework specifics (NDK, lifecycle).

• AI Fundamentals: Familiarize yourself with basic AI/ML concepts, especially related to model deployment on edge devices (e.g., model quantization, inference optimization).

📝 Enhancement Note: The interview process for a Senior/Staff role at Qualcomm will be rigorous, focusing heavily on technical depth and problem-solving ability. The portfolio review is a critical component; candidates must be able to clearly articulate their technical contributions and the impact of their work. Prepare to discuss performance metrics and optimization strategies extensively.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: C++, Java, Kotlin, C/C++ (Embedded)

• Development Environments: Android Studio, Visual Studio, GCC, Clang

• Version Control: Git (e.g., GitHub, GitLab, Bitbucket)

• Build Systems: CMake, Make, Gradle (for Android)

• Debugging Tools: GDB, LLDB, Android Debugger, Profilers (e.g., Perfetto, Valgrind)

Analytics & Reporting:

• Performance Profilers: Tools for analyzing CPU, memory, and power usage on target devices.

• Benchmarking Frameworks: Tools and methodologies for measuring and comparing performance of AI models and applications.

• Logging & Monitoring Tools: For debugging and understanding system behavior in real-time.

CRM & Automation:

• AI/ML Frameworks (for integration/prototyping): TensorFlow Lite, PyTorch Mobile, ONNX Runtime, potentially Qualcomm's proprietary AI engines.

• Android SDK/NDK: Essential for Android application development and native code integration.

• Linux Kernel/User Space: For embedded system development.

• Scripting Languages: Python for automation, tooling, and data processing.

📝 Enhancement Note: Candidates should be proficient in the core programming languages and development environments. Familiarity with AI/ML frameworks commonly used for on-device deployment is highly advantageous. Experience with performance profiling and debugging tools on embedded or mobile platforms is critical for this role.

👥 Team Culture & Values

Operations Values:

• Innovation & Curiosity: A drive to explore new AI technologies and find novel ways to implement them on devices.

• Technical Excellence: Commitment to high-quality code, robust engineering practices, and deep technical understanding.

• Collaboration & Teamwork: Working effectively with researchers, engineers, and cross-functional teams to achieve common goals.

• Impact & Results: Focusing on delivering tangible prototypes and demonstrating measurable performance improvements.

• Continuous Learning: Staying abreast of the rapidly evolving fields of AI and embedded systems.

Collaboration Style:

• Cross-Functional Integration: Close collaboration with AI researchers to understand model capabilities and limitations, and with systems engineers for hardware-specific optimizations.

• Proactive Communication: Regular updates, clear documentation, and open discussions to ensure alignment and address challenges promptly.

• Mentorship & Knowledge Sharing: A culture where senior members mentor junior engineers and knowledge is shared freely through code reviews, technical discussions, and documentation.

📝 Enhancement Note: Qualcomm's culture emphasizes innovation and technical rigor. For an AI Research role, expect a dynamic environment where challenging the status quo and pushing technological boundaries are encouraged. Collaboration is key, especially in bridging the gap between research and engineering.

⚡ Challenges & Growth Opportunities

Challenges:

• Performance Optimization on Constrained Devices: Balancing advanced AI features with the inherent limitations of mobile and embedded hardware (CPU, memory, power, thermals).

• Rapidly Evolving AI Landscape: Keeping pace with cutting-edge AI research and adapting new models and techniques for efficient on-device deployment.

• Bridging Research and Product: Translating experimental AI research into functional, performant prototypes that demonstrate real-world potential.

• Cross-Platform Complexity: Ensuring seamless integration and performance across different Snapdragon SoCs and Android versions.

Learning & Development Opportunities:

• Deep Dive into On-Device AI: Intensive exposure to the latest advancements in AI inference engines, model compression techniques, and AI hardware acceleration.

• Advanced System Optimization: Opportunities to master performance tuning, memory management, and power optimization techniques for complex embedded systems.

• Industry Conferences & Training: Participation in leading AI, mobile, and embedded systems conferences and specialized training programs.

• Mentorship from Experts: Learning from world-class AI researchers and seasoned software engineers within Qualcomm.

📝 Enhancement Note: The primary challenge lies in the intersection of cutting-edge AI with practical, resource-constrained hardware. Success requires a blend of AI understanding and deep systems engineering expertise. The growth opportunities are significant for those looking to specialize in the high-demand field of edge AI.

💡 Interview Preparation

Strategy Questions:

• On-Device AI Strategy: "How would you approach optimizing a large generative AI model for real-time inference on a mobile device with limited memory and processing power?" (Focus on model quantization, pruning, efficient inference engines, hardware acceleration).

• Collaboration with Researchers: "Describe a time you had to work with researchers who had different priorities or technical backgrounds. How did you ensure successful collaboration and prototype development?" (Focus on communication, empathy, finding common ground, translating technical concepts).

• Problem-Solving & Debugging: "Imagine a user reports that an AI feature on a prototype device is intermittently crashing or performing poorly. Walk me through your debugging process, from initial report to root cause analysis." (Focus on systematic approach, tools used, layered debugging).

Company & Culture Questions:

• "Why are you interested in working on on-device AI at Qualcomm, specifically in Vietnam?" (Highlight passion for AI, Qualcomm's leadership, interest in emerging markets/regional tech hubs).

• "How do you stay updated with the latest advancements in AI and software engineering?" (Mention specific resources, conferences, online courses, personal projects).

Portfolio Presentation Strategy:

• Structure: Use a clear, concise format for each project: Problem -> Your Role/Contribution -> Technical Approach -> Challenges -> Solution -> Results/Impact.

• Quantify Impact: Always use numbers. Instead of "improved performance," say "reduced inference latency by 30%," or "decreased memory usage by 150MB."

• Highlight Technical Depth: Be prepared to discuss the "why" behind your technical decisions. Explain trade-offs made and why your chosen solution was optimal.

• Focus on On-Device Relevance: For every project, try to draw parallels to the challenges of on-device AI development if possible (e.g., performance, resource constraints, real-time processing).

• Practice: Rehearse your presentation to ensure smooth delivery and stay within time limits. Be ready for spontaneous questions.

📝 Enhancement Note: Interview preparation should focus on demonstrating a strong command of core software engineering principles, a practical understanding of AI concepts relevant to deployment, and the ability to optimize for hardware constraints. Be ready to showcase your problem-solving skills through detailed examples from your portfolio and hypothetical scenarios.

📌 Application Steps

To apply for this Software Engineer position at Qualcomm:

• Submit your application: Utilize the provided link on the Qualcomm Careers website to submit your resume and any requested information.

• Tailor your resume: Ensure your resume clearly highlights your experience with C++, Android development, embedded systems, performance optimization, and any AI/ML integration work, using keywords from the job description. Quantify achievements whenever possible.

• Prepare your portfolio: Curate 2-3 key projects that best demonstrate your skills in software engineering, performance optimization, and on-device AI prototyping. Be ready to present these in detail during interviews.

• Research Qualcomm AI Research: Familiarize yourself with Qualcomm's AI initiatives, recent publications, and the types of applications they are targeting for on-device AI. Understand their competitive landscape.

• Practice interview questions: Rehearse answers to common technical, behavioral, and case-study questions, focusing on demonstrating your problem-solving abilities and technical depth relevant to this role.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.