AI Platform & Inference Suite Engineer (Staff/Senior Staff level) - Riyadh, KSA

About Us

Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including intelligent edge devices, next-generation computing platforms, and advanced AI solutions. Qualcomm’s leadership in AI, high ‑ performance compute, and connectivity is driving innovation across cloud, edge, and data center environments - delivering scalable, power ‑ efficient platforms that power the next generation of intelligent infrastructure.

About the Role

Qualcomm is seeking Machine Learning Applications Engineer – AI Inference & Model Optimization to support the enablement of rack-scale deep learning workloads on advanced Qualcomm AI inference accelerator s . These accelerators utilize Qualcomm's expertise in hardware-accelerated AI to deliver high-performance, energy-efficient generative AI and computer vision inference solutions for modern data centers.

This is a customer ‑ facing, highly technical role focused on porting, optimizing, and validating deep learning AI models on production systems, and enabling Qualcomm’s partners to develop and deploy advanced machine learning applications - including computer vision, speech, generative AI and state of the art multimodal reasoning models - using popular frameworks such as PyTorch , TensorFlow, and ONNX on Qualcomm Cloud AI accelerators. Key responsibilities include evaluating models for throughput, latency, and accuracy; profiling and optimizing model performance; building robust application pipelines; integrating customer frameworks; and contributing to documentation, training, and demonstrations.

The role requires strong expertise in AI models, quantization, performance optimization, and deployment, plus the ability to shape architecture, workload sizing, and system design. It also requires experience with deep learning model development across hardware platforms, solid programming skills, collaboration with cross-functional teams, and proficiency in machine learning frameworks, Linux, and container orchestration tools.

The ideal candidate can effectively bridge AI model requirements hardware capabilities customer expectations , guiding customers from model selection hardware sizing deployment decisions production readiness.

What You’ll Do

A) AI Model Porting & Optimization

• Deploy, optimize and scale deep learning AI models onto accelerator ‑ based data center platforms, including:

• Model conversion workflows

• Quantization techniques (INT8 / mixed precision)

• Runtime integration and optimization

• Integrate ML models onto Qualcomm’s Cloud AI ML stack from frameworks such as PyTorch , TensorFlow, and ONNX.

• Drive improvements in model throughput , latency , and accuracy , with clear trade ‑ off analysis.

• Build, test, and deploy scalable inference pipelines using serving frameworks such as vLLM , TGI, and Triton.

• Optimize workloads for LLM and GenAI models across both multi-SoC and multi-card architectures.

• Collaborate with engineering teams to analyze and refine training and inference for advanced deep learning applications.

• Identify bottlenecks across compute, memory, and runtime, and guide optimization strategies.

• Contribute to Qualcomm’s Cloud AI GitHub repository and developer documentation, sharing technical best practices and solutions.

• Develop and integrate end-to-end ML application pipelines with customer frameworks and libraries.
  

B) Customer ‑ Facing Technical Engagement

• Act as a trusted technical advisor for customers deploying AI workloads.

• Engage in hardware sizing and architecture discussions , aligning model requirements with infrastructure capabilities.

• Provide technical guidance on:

• AI model selection

• Deployment feasibility

• System architecture and performance expectations

• Lead discussions on model capabilities and limitations based on real customer use cases.
  

C) Model–Infrastructure Alignment

• Assess and evaluate AI model requirements and recommend alternative model approaches when necessary.

• Align model characteristics (latency, throughput, accuracy) with accelerator and system capabilities .

• Connect model requirements with:

• Memory constraints

• Accelerator architecture

• Scaling limitations

• Support customers in defining model selection strategies based on deployment realities .
  

D) Performance & Scalability Engineering

• Evaluate performance characteristics of AI models in production scenarios, including:

• Throughput expectations

• Latency targets

• Concurrency behavior

• Guide architecture decisions around:

• Scaling strategies (horizontal vs vertical)

• Hardware deployment sizing

• Contribute to discussions on:

• Workload scalability limits

• Impact of model selection on system performance and efficiency

• Provide insights into capacity planning and infrastructure optimization .
  

E) End ‑ to ‑ End AI Pipeline Design

• Drive discussions around end ‑ to ‑ end AI pipelines , including:

• Multi ‑ model workflows (e.g., detection + tracking + recognition)

• Data preprocessing and post ‑ processing stages

• Guide decisions on video and data processing stacks, including:

• Video pipeline choices (e.g., FFMPEG vs GStreamer )

• Integration into inference pipelines

• Ensure pipelines are aligned with:

• Performance requirements

• Hardware capabilities

• Real ‑ time constraints
  

F) Model Trade ‑ off Analysis & Validation

• Highlight and explain trade ‑ offs between:

• Accuracy vs compatibility

• Model quality vs deployment feasibility

• Support decision ‑ making on:

• Model simplification vs performance gains

• Precision vs efficiency trade ‑ offs

• Lead or support model capability validation in deployment environments.

• Collaborate with customers to define:

• Inference assumptions

• Model sizing strategies for large ‑ scale workloads
  

Required Qualifications

• Bachelor’s degree in Computer Science , Computer Engineering, Electrical Engineering, or related field (or equivalent experience).

• 10–15+ years of experience in:

• D eep learning model development or deployment experience on CPUs/GPUs/ASICs.

• Inference systems and optimization

• Data center or edge AI platforms

• Strong experience with:

• Model quantization and optimization techniques

• AI model frameworks (e.g., PyTorch , TensorFlow)

• Model deployment pipelines

• Excellent C/C++/Python programming and software design skills, including debugging, and performance analysis.

• Hands on expertise with Linux-based systems, low level software, drivers, and system bring up.

• Proven ability to analyze and optimize model performance in production environments.

• Solid understanding of:

• AI inference hardware constraints

• System level performance bottlenecks

• Strong communication skills and experience in customer facing technical roles.

• Willingness to travel for customer engagements and strategic reviews.
  

Preferred Qualifications

• Skilled in deploying models on platforms that use hardware accelerators for inference.

• Experienced with managing multi-model workflows and building real-time AI systems, including computer vision, video, and analytics projects.

• Knowledgeable about distributed inference methods and handling large-scale model deployments.

• Proficient in developing and maintaining video processing workflows and using relevant software frameworks.

• Deep understanding of how system-level decisions affect performance in actual deployment environments.

• Capable of simplifying complex technical ideas into straightforward, useful advice for clients.

• Hands-on experience running deep learning models on popular ML frameworks such as PyTorch , TensorFlow, ONNX

• Experience developing software solutions that run in Linux environments with containers and orchestration

• Experience with Source code and configuration management tools, Git knowledge is required.

• Customer-facing experience translating customer requirements into technical solutions (discovery, scoping, success criteria, and execution plans).

• Proven ability to build and deliver technical demos, proofs-of-concept, and reference applications for ML/GenAI workloads.

• Strong technical writing skills to produce customer-ready documentation (getting started guides, deployment runbooks, troubleshooting guides) and deliver partner training sessions.

• Experience driving issue triage and technical escalations with customers, coordinating across product , hardware, and software engineering teams to resolution.

• Excellent stakeholder management and communication skills: present complex technical concepts clearly to both engineering and non-engineering audiences.