AI Platform & Inference Suite Engineer​/Senior level KSA

About the Role

Qualcomm is seeking a Machine Learning Applications Engineer – AI Inference & Model Optimization to support the enablement of rack‑scale deep learning workloads on advanced Qualcomm AI inference accelerators. This customer‑facing, highly technical role focuses 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, Tensor Flow, and ONNX on Qualcomm Cloud AI accelerators.

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.

• 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, and integration onto Qualcomm’s Cloud AI ML stack from frameworks such as PyTorch, Tensor Flow, 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 Git Hub repository and developer documentation, sharing technical best practices and solutions.
• Develop and integrate end‑to‑end ML application pipelines with customer frameworks and libraries.

• 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.

• 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, and scaling limitations.
• Support customers in defining model selection strategies based on deployment realities.

• Evaluate performance characteristics of AI models in production scenarios, including throughput expectations, latency targets, and concurrency behavior.
• Guide architecture decisions around scaling strategies (horizontal vs vertical) and hardware deployment sizing.
• Contribute to discussions on workload scalability limits and impact of model selection on system performance and efficiency.
• Provide insights into capacity planning and infrastructure optimization.

• Drive discussions around end‑to‑end AI pipelines, including multi‑model workflows (e.g., detection + tracking + recognition).
• Guide decisions on video and data processing stages, such as video pipeline choices (FFMPEG vs GStreamer) and integration into inference pipelines.
• Ensure pipelines are aligned with performance requirements, hardware capabilities, and real‑time constraints.

• Highlight and explain trade‑offs between accuracy vs compatibility and model quality vs deployment feasibility.
• Support decision‑making on model simplification…