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Director Engineering - Ai Software Stack

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Onsite

Director Engineering - Ai Software Stack

Qualcomm

Director Engineering - Ai Software Stack

Skills

PythonJavaPyTorchTensorFlowMachine LearningAILinux

2 months ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

24 August 2026

What they need


General Summary:

As a leading technology innovator, Qualcomm pushes the boundaries of what''s possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Software Engineer, you will design, develop, create, modify, and validate embedded and cloud edge software, applications, and/or specialized utility programs that launch cutting-edge, world class products that meet and exceed customer needs. Qualcomm Software Engineers collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces.

Key Responsibilities

  • AI/ML Strategy Platform Leadership
  • Define and own the AI/ML technology roadmap aligned with business goals across automotive and edge AI platforms.
  • Drive adoption of LLMs, VLMs/LVMs, multimodal AI, and agentic AI systems for realworld, safetycritical deployments.
  • Influence platform and siliconlevel decisions by translating AI workload requirements into hardware and software capabilities.

Minimum Qualifications:

Bachelor''s degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience. ORMaster''s degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience. ORPhD in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience. 2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc.

Execution Delivery

  • Oversee endtoend delivery of AI systems from research to production, including model onboarding, optimization, validation, and lifecycle management.
  • Ensure performance, power, latency, memory, and accuracy targets are met across heterogeneous compute (CPU/GPU/DSP/NPU).
  • Establish best practices for model optimization (quantization, compression, batching, KVcache tuning, mixed precision).

Team Organizational Leadership

  • Build, mentor, and scale multidisciplinary AI/ML engineering teams (ML engineers, systems engineers, platform engineers).
  • Set technical direction, define career growth paths, and uphold a high bar for engineering excellence.
  • Foster a culture of ownership, accountability, and innovation.

Minimum Qualifications

  • Bachelors degree in Engineering, Computer Science, or a related field.
  • 25+ years of industry experience in software engineering.
  • 20+ years of people management experience, including managing managers or senior technical leads and customers.
  • Strong background in AI/ML system design, not just model development.
  • Handson experience with ML frameworks (PyTorch, TensorFlow, ONNX) and edge/embedded deployment.
  • Proven ability to deliver largescale, production AI systems in complex environments

Preferred Qualifications

  • Masters or PhD in AI/ML, Computer Science, or related field.
  • Deep understanding of LLMs, VLMs/LVMs, multimodal and agentic AI architectures.
  • Experience with automotive or industrial platforms, embedded Linux/QNX, and realtime constraints.
  • Familiarity with QNNlike runtimes, inference SDKs, and heterogeneous acceleration.
  • Strong customerfacing experience, including executivelevel technical discussions.
  • Track record of influencing product and platform roadmaps through technical leadership.
  • Experience in software architecture and design patterns
  • Knowledge of architectural approaches for large-scale software applications
  • Previous customer-facing experience is advantageous
  • Exposure to fine-tuning GenAI models and reinforcement learning considered a plus
  • Expertise in inference accuracy, throughput optimization, and edge deployment highly desirable
  • Lead system-level problem triage to identify root causes and communicate testing/debugging results to relevant stakeholders

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