Senior Software Engineer (On-Device AI Prototyping)

📍 Job Overview

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

Company: Qualcomm Vietnam Company Limited

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

Job Type: Full-Time

Category: Software Engineering / AI Research & Development

Date Posted: 2026-09-07

Experience Level: Mid-Senior (2-5 years)

Remote Status: On-site

🚀 Role Summary

• This role focuses on the rapid development and prototyping of agentic AI systems, bridging cutting-edge AI research with practical applications on Qualcomm's platforms.

• You will collaborate closely with AI researchers to transform emerging AI technologies into compelling end-to-end experiences, with a strong emphasis on on-device AI capabilities.

• The position requires a blend of strong software engineering fundamentals and hands-on experience in building complex AI-powered applications, particularly those involving foundation models, tool integration, memory, planning, and reasoning.

• Key activities include designing and implementing applications, frameworks, and system components to showcase Qualcomm's AI prowess, with a focus on agentic AI workflows and system integration.

📝 Enhancement Note: This role is positioned within Qualcomm AI Research, indicating a focus on innovation and future-generation technologies. The emphasis on "agentic AI systems" and "on-device AI" suggests a strategic direction for Qualcomm in leveraging AI for intelligent edge devices. The "Senior" title, coupled with the 2-5 year experience requirement, points to a role that expects independent contribution and a strong foundation in software development principles.

📈 Primary Responsibilities

• Develop prototype applications and system components that showcase advanced AI capabilities, particularly focusing on agentic AI.

• Collaborate with AI researchers to transform emerging AI technologies and research concepts into compelling, end-to-end agentic experiences.

• Rapidly prototype, evaluate, and iterate on new agent architectures, workflows, and user experiences, ensuring feasibility and performance.

• Build proof-of-concept systems that integrate AI agents with mobile, edge, cloud, and specific device capabilities, such as Snapdragon platforms.

• Architect clean, maintainable, and extensible software systems that support rapid experimentation, iteration, and integration of new AI models and techniques.

• Contribute to the development and enhancement of internal frameworks, tools, and infrastructure that accelerate AI prototyping and research validation.

• Design and implement software components, frameworks, and end-to-end prototypes for agent-enabled applications that combine foundation models, tools, APIs, external services, and device capabilities.

• Evaluate emerging agent architectures, workflows, and user experiences, providing practical feedback on feasibility, performance bottlenecks, system constraints, and user experience.

• Integrate and evaluate AI models and agentic workflows, including tool integration, orchestration logic, memory systems, runtime integration, and end-to-end data flows, working closely with AI researchers and system engineers.

• Drive high-quality software design, architecture, testing, and maintainability for AI prototypes and supporting systems.

• Build internal tools and automation to improve engineering productivity and prototyping velocity for the AI research team.

• Partner closely with researchers and engineers across different disciplines to deliver impactful prototypes and research demonstrations.

• Communicate technical designs, trade-offs, implementation details, and findings effectively to both technical and non-technical stakeholders.

📝 Enhancement Note: The responsibilities highlight a dual focus on both the technical implementation of AI systems and the strategic integration of research into tangible prototypes. The emphasis on "agentic AI," "on-device AI," and "Snapdragon platforms" is crucial for candidates to understand the specific domain of this role within Qualcomm. The expectation to "build internal tools and automation" suggests a need for engineers who can also improve the efficiency of the R&D process.

🎓 Skills & Qualifications

Education:

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

• Master’s degree or PhD in a related field is a strong plus and may reduce required years of experience. Experience:

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

• Minimum of 3+ years of software development experience with a Master's degree.

• Minimum of 2+ years of software development experience with a PhD.

• Equivalent industry experience in systems or mobile development may be considered.

• Proven experience in designing and building complex software systems with clean, maintainable code.

• Demonstrated experience developing applications or systems from concept to a working prototype. Required Skills:

• Strong programming proficiency in one or more languages such as Python, C++, Java, Kotlin, Go, or Rust.

• Solid software engineering fundamentals, including architecture design, modularity, testing, debugging, and performance optimization.

• Experience designing and building complex software systems with a focus on clean, maintainable code.

• Proven ability to develop applications or systems from concept to a working prototype.

• Excellent problem-solving capabilities and strong collaboration skills. Preferred Skills:

• Hands-on experience building agentic AI applications leveraging Large Language Models (LLMs), multimodal models, tool calling, workflow orchestration, memory systems, planning, reasoning, or multi-agent architectures.

• Familiarity with agent frameworks such as LangGraph, Semantic Kernel, Microsoft Cognitive Toolkit (MCP), or similar technologies.

• Experience evaluating and improving agent reliability, task completion rates, and overall user experience.

• Proven ability to rapidly prototype AI-powered applications and translate research concepts into working demonstrations.

• Experience integrating AI-powered features across mobile, edge, cloud, or embedded platforms.

• Familiarity with Snapdragon platforms or experience with on-device AI deployment is highly desirable.

• Experience building internal tools, automation systems, or developer productivity solutions.

📝 Enhancement Note: The preferred skills section is critical for candidates interested in this role. It explicitly calls out experience with "agentic AI," specific frameworks like "LangGraph" and "Semantic Kernel," and "on-device AI" on "Snapdragon platforms." These are key indicators of the specialized knowledge Qualcomm is seeking. The broad range of programming languages (Python, C++, Java, Kotlin, Go, Rust) indicates flexibility and a need for strong foundational skills.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase projects demonstrating the ability to translate complex research concepts into functional prototypes, especially in AI.

• Include examples of architecting and building robust, scalable, and maintainable software systems.

• Highlight contributions to internal tools, frameworks, or automation that improved development efficiency or prototyping velocity.

• Present projects that demonstrate integration of AI models with various platforms (mobile, edge, cloud, device-specific). Process Documentation:

• Provide documentation for at least one significant project that outlines the architecture design, key components, and integration points of an AI system.

• Detail the iterative prototyping process, including challenges faced, solutions implemented, and performance optimizations made.

• Include examples of how you evaluated and improved the reliability, performance, or user experience of a software system or AI application.

• Demonstrate experience in developing and documenting internal tools or automation scripts that streamlined development workflows.

📝 Enhancement Note: For a role focused on AI prototyping and research integration, a portfolio is essential. Candidates should be prepared to demonstrate not just finished products, but the process of rapid development, iteration, and the ability to document complex technical designs. The emphasis on "agentic AI workflows" and "on-device AI" means portfolio pieces should ideally reflect these specific areas.

💵 Compensation & Benefits

Salary Range:

Given the location (Hanoi, Vietnam; Ho Chi Minh City, Vietnam) and the Senior Software Engineer title with specific AI expertise, a competitive salary range is expected. Based on industry benchmarks for similar roles in Vietnam, the estimated annual salary range is likely between 1,200,000,000 VND to 2,500,000,000 VND (approximately $50,000 - $105,000 USD, depending on the exchange rate). This range can vary significantly based on the candidate's exact experience, specific skill set in AI/ML, and negotiation.

Benefits:

• Comprehensive health insurance coverage (medical, dental, vision).

• Paid time off, including vacation days, sick leave, and public holidays.

• Opportunities for professional development, including training, workshops, and conference attendance.

• Access to Qualcomm's cutting-edge technology and research environments.

• Potential for performance-based bonuses and stock options (subject to company policy).

• Relocation assistance may be available for candidates moving to Vietnam.

• Opportunities to work on high-impact, next-generation AI technologies.

• Retirement savings plans or equivalent benefits. Working Hours:

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

• Flexibility may be offered, but the role is designated as on-site, requiring regular in-office presence.

• Occasional overtime may be necessary to meet project deadlines or support critical research initiatives.

📝 Enhancement Note: Salary estimation for Vietnam is based on general market research for senior software engineering roles with specialized AI/ML skills. The actual compensation will depend heavily on Qualcomm's internal compensation bands, the candidate's negotiation skills, and the specific depth of their expertise in agentic AI and on-device ML. Benefits are standard for large tech companies, with a focus on professional growth and exposure to advanced AI research.

🎯 Team & Company Context

🏢 Company Culture

Industry: Semiconductor, Technology, Artificial Intelligence, Mobile Technology. Qualcomm is a global leader in wireless technology and a pioneer in mobile innovation, now heavily investing in AI research and development for its next-generation platforms.

Company Size: Qualcomm is a large, multinational corporation with over 50,000 employees globally. This means the company offers a stable, structured environment with extensive resources and opportunities for career growth.

Founded: Qualcomm was founded in 1985. Its long history in the tech industry signifies stability, deep expertise, and a track record of innovation.

Team Structure:

• The role is within the Qualcomm AI Research group, which is dedicated to pushing the boundaries of AI technology.

• The team likely comprises highly skilled AI researchers, software engineers, and system architects.

• The structure is expected to be collaborative, with cross-functional teams working on specific AI research initiatives and prototyping efforts.

• Reporting lines will likely be within an engineering management structure, with close collaboration with research leads. Methodology:

• The team operates at the intersection of cutting-edge AI research and practical software engineering.

• Emphasis is placed on rapid prototyping, agile development cycles, and iterative experimentation to validate research concepts.

• Data-driven decision-making is paramount, utilizing performance metrics and user feedback to refine prototypes.

• A strong focus on software engineering best practices ensures that prototypes are robust, maintainable, and can serve as a foundation for future product development.

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

📝 Enhancement Note: Qualcomm's culture is known for its strong engineering focus, innovation, and a commitment to pushing technological boundaries. For an AI Research role, expect a highly intellectual and collaborative environment where scientific curiosity meets engineering rigor. The scale of Qualcomm provides significant resources and global reach for its projects.

📈 Career & Growth Analysis

Operations Career Level: This is a Senior Software Engineer role, typically falling into the mid-to-late career stage for individual contributors. It requires a strong technical foundation, the ability to work independently, and the capacity to mentor junior engineers. The focus on AI prototyping suggests a specialized technical track within software engineering.

Reporting Structure: The Senior Software Engineer will likely report to an Engineering Manager or a Lead Engineer within the AI Research division. They will collaborate closely with AI Researchers, Project Managers, and potentially product teams.

Operations Impact: This role has a direct impact on Qualcomm's future product strategy by accelerating the integration of advanced AI capabilities, particularly agentic AI and on-device intelligence, into their core technologies and platforms like Snapdragon. Successful prototypes can directly influence product roadmaps and provide a competitive edge.

Growth Opportunities:

• Technical Specialization: Deepen expertise in agentic AI, on-device ML, and specific AI frameworks, potentially becoming a subject matter expert.

• Leadership Development: Transition into a technical lead role, guiding smaller teams or specific project streams within AI research.

• Cross-Functional Mobility: Move into related areas such as AI platform development, product engineering for AI features, or performance optimization for AI on edge devices.

• Research Collaboration: Further enhance collaboration with leading AI researchers, contributing to publications or patents.

• Mentorship: Guide and mentor junior engineers, sharing expertise in AI and software development best practices.

📝 Enhancement Note: The "Senior" title and the focus on cutting-edge AI research indicate significant growth potential. Candidates can expect to develop highly sought-after skills in agentic AI and on-device intelligence, which are critical for the future of mobile and edge computing. The opportunity to work on foundational technologies for Qualcomm's flagship products is a key career differentiator.

🌐 Work Environment

Office Type: This is an on-site role, implying a traditional office environment within Qualcomm's facilities in Hanoi or Ho Chi Minh City. These facilities are typically modern, well-equipped, and designed to foster collaboration.

Office Location(s):

• Hanoi, Vietnam: Candidates can expect to work from Qualcomm's established office in the capital city, likely in a business district.

• Ho Chi Minh City, Vietnam: Similarly, the role would be based at Qualcomm's office in Vietnam's largest economic hub. Workspace Context:

• Collaborative workspaces designed to encourage interaction between researchers, engineers, and project teams.

• Access to advanced computing resources, development tools, and potentially specialized hardware for AI prototyping.

• A professional atmosphere with opportunities for networking and knowledge sharing within the broader Qualcomm engineering community.

• The environment will support both focused individual work and dynamic team collaboration essential for rapid prototyping cycles. Work Schedule:

• Standard business hours, Monday to Friday, with an expectation of 40 hours per week.

• Given the R&D and prototyping nature of the role, some flexibility may be available, but consistent on-site presence is required.

• The fast-paced nature of AI research may occasionally require working beyond standard hours to meet critical project milestones or experimental deadlines.

📝 Enhancement Note: The on-site requirement is significant. Candidates should be prepared for a structured work environment that prioritizes in-person collaboration and access to company resources, which is often crucial for hardware-adjacent R&D like AI prototyping on specific platforms.

📄 Application & Portfolio Review Process

Interview Process:

1. Application Screening: Initial review of resumes and qualifications against the job requirements, with a focus on AI/ML experience, software engineering skills, and relevant programming languages.

2. Technical Phone/Video Interview: A screening interview with an engineer or hiring manager to assess foundational software engineering knowledge, understanding of AI concepts, and initial fit with the role. This may include coding challenges.

3. On-site/Virtual On-site Interviews: A series of interviews (typically 3-5 sessions) covering:

• Deep Technical Dive: In-depth discussions on software architecture, system design, algorithms, and specific AI/ML concepts. Expect coding exercises and system design problems.
• AI/ML Prototyping Focus: Questions centered on experience with agentic AI, LLMs, prototyping workflows, and integrating AI models.
• Behavioral & Situational Questions: Assessing problem-solving skills, teamwork, communication, and adaptability, particularly in a research-driven environment.
• Portfolio Review: A dedicated session to walk through selected projects from your portfolio, explaining your role, technical challenges, solutions, and impact.

1. Hiring Manager/Team Lead Interview: A final discussion to assess overall fit with the team, discuss career aspirations, and confirm alignment with the role's strategic objectives.

Portfolio Review Tips:

• Curate Strategically: Select 2-3 projects that best showcase your experience in AI prototyping, agentic systems, software architecture, and on-device development.

• Focus on Process: For each project, clearly articulate the problem statement, your specific contributions, the technical challenges, your design decisions (and trade-offs), the implementation details, and the outcomes/impact.

• Highlight AI Relevance: Emphasize how your projects involved AI models (especially LLMs), agentic workflows, tool integration, or on-device deployment. If you used frameworks like LangGraph or Semantic Kernel, detail your experience.

• Quantify Impact: Where possible, use metrics to demonstrate the success of your prototypes (e.g., performance improvements, task completion rates, efficiency gains).

• Prepare for Technical Deep Dive: Be ready to answer detailed questions about the code, architecture, and technologies used in your portfolio projects.

Challenge Preparation:

• Coding Fundamentals: Practice coding problems in Python, C++, or other relevant languages, focusing on data structures, algorithms, and efficient problem-solving.

• System Design: Prepare for system design questions, focusing on scalability, reliability, and modularity, especially in the context of AI systems.

• AI/ML Concepts: Brush up on core AI concepts, LLMs, agent architectures, and the practical aspects of deploying AI on edge devices. Understand concepts like tool calling, memory, and planning.

• Behavioral Scenarios: Prepare examples using the STAR method (Situation, Task, Action, Result) to illustrate your problem-solving, teamwork, and leadership skills.

📝 Enhancement Note: The interview process will be rigorous, testing both deep technical expertise in software engineering and AI, as well as the ability to translate research into practical applications. A well-prepared portfolio that specifically addresses agentic AI and on-device prototyping is crucial for success.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (highly probable for AI/ML), C++, Java, Kotlin, Go, Rust.

• AI/ML Frameworks: PyTorch, TensorFlow (likely for underlying model development or integration).

• Agentic AI Frameworks: LangGraph, Semantic Kernel, Microsoft Cognitive Toolkit (MCP), or similar orchestration and agent development tools.

• Development Environments: IDEs like VS Code, PyCharm, CLion; version control systems like Git.

Analytics & Reporting:

• Data Analysis Libraries: Pandas, NumPy (for Python).

• Performance Profiling Tools: Tools specific to C++ or Python for analyzing code execution and identifying bottlenecks.

• Experiment Tracking: Tools like MLflow, Weights & Biases (W&B) may be used for managing AI experiments.

CRM & Automation:

• Not directly applicable to this R&D prototyping role, but familiarity with CI/CD pipelines (e.g., Jenkins, GitLab CI) and scripting for automation (e.g., Bash) would be beneficial.

• Experience with containerization technologies like Docker and orchestration platforms like Kubernetes might be relevant for deploying complex prototypes.

📝 Enhancement Note: The technology stack emphasizes modern AI development practices. Python is almost certainly the primary language for AI/ML tasks, while C++ might be used for performance-critical components or on-device implementations. Familiarity with specific agent frameworks like LangGraph is a significant plus.

👥 Team Culture & Values

Operations Values:

• Innovation & Curiosity: A strong drive to explore new AI research and develop novel solutions.

• Engineering Excellence: Commitment to building high-quality, robust, and maintainable software.

• Collaboration & Teamwork: Working effectively with researchers and engineers across different disciplines.

• Impact & Delivery: Focus on transforming research into tangible prototypes and demonstrations that showcase Qualcomm's capabilities.

• Problem-Solving: A proactive and analytical approach to tackling complex technical challenges.

• Continuous Learning: Staying abreast of the rapidly evolving field of AI and software development.

Collaboration Style:

• Highly collaborative, with close interaction between AI researchers and software engineers.

• Emphasis on open communication, sharing ideas, and constructive feedback.

• Cross-functional teamwork is essential, involving individuals from research, engineering, and potentially product teams.

• A culture that encourages experimentation and learning from failures as part of the R&D process.

📝 Enhancement Note: The culture is expected to be intellectually stimulating, fast-paced, and driven by innovation. Engineers will need to be comfortable working at the forefront of AI research and translating complex ideas into practical, demonstrable solutions.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving Field: Keeping pace with the continuous advancements in AI research and development, particularly in agentic AI.

• Bridging Research and Product: Translating cutting-edge, often theoretical, research into functional, efficient, and demonstrable prototypes for real-world platforms.

• On-Device Constraints: Optimizing AI models and agentic workflows to run effectively within the resource constraints of mobile and edge devices.

• Integration Complexity: Integrating diverse AI models, tools, and external services into cohesive agentic systems.

• Ambiguity in Research: Working with research concepts that may not yet have well-defined specifications or established best practices.

Learning & Development Opportunities:

• Cutting-Edge AI Exposure: Direct involvement with state-of-the-art AI research and technologies from a leading AI research organization.

• Skill Specialization: Opportunity to become an expert in agentic AI, on-device AI, and specific AI frameworks.

• Cross-Disciplinary Learning: Gaining insights into AI research methodologies and understanding the nuances of hardware-platform integration.

• Professional Development: Access to internal training, workshops, and potentially external conferences relevant to AI and software engineering.

• Mentorship: Learning from experienced researchers and engineers within Qualcomm.

📝 Enhancement Note: The challenges are inherent to working in AI R&D. The growth opportunities, however, are significant, offering a chance to build highly valuable and in-demand skills in a rapidly expanding technological frontier.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex software system you designed and built from concept to prototype. What were the key architectural decisions, and what trade-offs did you make?" (Focus on demonstrating system design skills, understanding of trade-offs, and the prototyping process.)

• "How would you approach building an agentic AI system that can interact with external tools and maintain context over long conversations?" (Prepare to discuss agent architectures, LLM integration, tool calling mechanisms, and memory systems.)

• "Walk me through your experience with [specific AI framework like LangGraph or Semantic Kernel]. What challenges did you face, and how did you overcome them?" (Be ready to detail practical application and problem-solving with these tools.)

• "How do you ensure software quality and maintainability when working in a fast-paced prototyping environment?" (Discuss testing strategies, modular design, and documentation practices.) Company & Culture Questions:

• "What interests you about Qualcomm AI Research and this specific role focusing on agentic AI prototyping?" (Show genuine interest in the company's AI initiatives and the role's technical focus.)

• "How do you stay updated with the latest advancements in AI and software engineering?" (Highlight your commitment to continuous learning.)

• "Describe a time you had to collaborate with researchers or engineers from a different discipline. How did you ensure effective communication and achieve a common goal?" (Use STAR method to showcase collaboration skills.) Portfolio Presentation Strategy:

• Structure Your Narrative: For each project, clearly outline the problem, your role, the technical solution (architecture, algorithms, tools), the challenges, and the impact/results.

• Emphasize AI & Prototyping: Clearly articulate the AI components, the agentic workflow, and the rapid prototyping nature of the project.

• Technical Depth: Be prepared to dive deep into the technical details, code snippets, and design decisions.

• Quantify Results: Use metrics to demonstrate the success and impact of your prototypes.

• Relevance: Ensure the projects selected directly align with the skills and technologies mentioned in the job description (agentic AI, on-device AI, specific languages, frameworks).

📝 Enhancement Note: The interview will heavily assess practical application of AI concepts and strong software engineering principles. Candidates should be ready to demonstrate their ability to not just understand AI research but to actively build and prototype with it.

📌 Application Steps

To apply for this Senior Software Engineer position:

• Submit your application through the Qualcomm Careers portal using the provided URL.

• Tailor Your Resume: Highlight experience with AI/ML, agentic AI, prototyping, and specific programming languages (Python, C++, etc.) and frameworks (LangGraph, Semantic Kernel). Quantify achievements where possible.

• Prepare Your Portfolio: Select 2-3 key projects that best demonstrate your skills in AI prototyping, agentic systems, and software architecture. Be ready to present and discuss these in detail.

• Research Qualcomm AI: Familiarize yourself with Qualcomm's AI initiatives, their focus on on-device AI, and the Snapdragon platform. Understand their position in the semiconductor and mobile technology markets.

• Practice Technical & Behavioral Questions: Prepare for coding challenges, system design questions, and behavioral scenarios, focusing on your experience with AI and collaborative R&D environments.

⚠️ 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.