Company:

Qualcomm Vietnam Company Limited, Hanoi Branch Office

Job Area:

Engineering Group, Engineering Group > Machine Learning Engineering

General Summary:

At Qualcomm AI Research Vietnam, the GenAI Engineer plays a critical role in accelerating adoption of strategic Generative AI models by rapidly evaluating, onboarding, and optimizing LLMs, VLMs, and multimodal models on Qualcomm platforms, including mobile, compute, XR, and other edge AI platforms. This role provides early deployment feasibility and performance insights to support informed product, customer, and business decisions, while delivering production-ready on-device AI experiences that meet latency, memory, power, and accuracy requirements.

Key Responsibilities

• Model Evaluation & Onboarding: Evaluate strategic GenAI models, including LLMs, VLMs, and multimodal models, and drive onboarding to Qualcomm AI software stacks and target platforms.
• Quantization & Optimization Enablement: Enable quantization, model adaptation, and performance optimization workflows to meet platform constraints for latency, memory, power, and accuracy.
• On-device Validation & Benchmarking: Execute on-device validation, downstream benchmarking, and performance analysis to provide early feasibility insights and readiness signals.
• Cross-stack Debugging: Identify issues early across model, quantization, runtime, backend, and system layers, and drive resolution with system, compiler, runtime, and platform engineering teams.
• Rapid Prototype Enablement: Provide early inference support and prototype enablement for new model architectures before the broader ecosystem is fully mature.
• Production-readiness Delivery: Translate research models and early technology IP into robust on-device AI experiences suitable for productization, demos, customer evaluation, and business decision-making.
• Documentation & Knowledge Sharing: Maintain clear technical documentation, benchmark reports, debugging notes, and onboarding guides to improve team execution and cross-team collaboration.

Requirements

Education & Background

Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Artificial Intelligence, Machine Learning, or a related technical field.

Hands-on experience with AI/ML model deployment, optimization, benchmarking, or software development for edge, mobile, compute, or embedded platforms.

Technical & Domain Knowledge

Strong understanding of GenAI model architectures such as LLMs, vision-language models, diffusion models, or multimodal models.

Experience with model optimization techniques such as quantization, graph conversion, profiling, memory analysis, and runtime performance tuning.

Familiarity with AI software stacks, inference runtimes, hardware accelerators, and debugging workflows across model, runtime, backend, and system layers.

Ability to analyze benchmark results, identify bottlenecks, and communicate deployment feasibility and performance trade-offs clearly.

Soft Skills

Strong ownership mindset with the ability to drive technical execution across ambiguous and fast-moving projects.

Excellent problem-solving and debugging skills, with the ability to break down complex cross-stack issues and coordinate resolution with partner teams.

Clear communication skills to present technical findings, risks, trade-offs, and recommendations to engineering, product, and leadership stakeholders.

Preferred Qualifications

Experience with Qualcomm AI software stacks, QNN, NPU acceleration, or similar edge AI deployment toolchains.

Experience onboarding or benchmarking LLMs, VLMs, multimodal models, diffusion models, or other modern GenAI architectures.

Experience collaborating with research, quantization, backend, runtime, system, and product teams to deliver production-ready AI features.

Minimum Qualifications:

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• OR
• Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• OR
• PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

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