General Summary:
- As part of Qualcomm AI Systems Solution CoE (Center of Excellence) team, you will develop leading-edge products and solutions around best-in-class Qualcomm high-performance inference accelerators for cloud, edge and hybrid AI applications. In this role, you will be developing and delivering System solution software for requirements across automotive, cloud and industrial IoT BUs, and drive overall system solutions. This covers a broad range of technical tasks, starting from industry leading and customer specific use cases, a spectrum of technologies to define AI/ML system solutions, ML models optimization (including GenAI/ LLMs/ LVMs/ LMMs) and ensure efficient and scalable deployment on inference accelerators and edge devices of different formfactors (phone, IHH, AI PC, AI Appliance).
- Qualcomm AI Systems engineers collaborate across functional teams to meet and exceed system-level requirements and standards. This is your opportunity to work on the leading-edge AI technologies and systems, current product and future roadmap designed for AI inference acceleration and to addresses unique requirements in energy efficient AI compute.
- In this leadership role, you will be engaged with key customers and internal hardware and software teams across automotive, cloud and industrial IoT BUs, product and project management, business development leads and other key stakeholders/executives and partners to drive overall system solutions. This covers a broad range of technical tasks, starting from industry leading and customer specific use cases, a spectrum of technologies to define AI/ML system solutions, ML models (including GenAI/LLMs) and optimization and efficient and scalable deployment on inference accelerators and edge devices of different formfactors (phone, IHH, AI PC, AI Appliance).
Job Description
Required Skills and Aptitudes
- Leadership experience on projects from architecture, development, design, deployment to production
- Hands-on experience in ML/AI tools development involving Deep Learning frameworks like Pytorch 1.x, 2.x, TensorFlow, ONNX, etc
- Strong understanding of model architectures across CV, NLP, LLMs, LVMs and Multi-Modal NWs
- Strong understanding and experience in working on inference graph optimization, Quantization, pruning and compression techniques etc.
- Understanding of performance and accuracy metrics for different class of neural NWs
- Detailed understanding of AI edge and server systems, infrastructure and industry standards
- Understanding of performance and accuracy metrics for different class of neural NWs
- Background of HW architectures like GPUs, AI accelerators like TPUs will be a plus
- Hands-on Experience with Object Orientated Design, design patterns
- Strong python / C++ implementation skills (Skill level > 7/10)
- Strong in Data structures & Algorithms (Skill level > 7/10)
- Hands on knowledge of LLM fine tuning, Quantization aware training is a plus
- Knowledge of machine learning runtimes like ONNX Runtime, Pytorch and TF runtimes is a plus
- Experience in survey of competitor toolchain and survey latest trends in the industry and academia
- Hands-on experience and necessary software skills to develop both prototype and production quality solutions
- Develop new and innovative ideas (e.g. IDFs) for a product or feature area.
- Excellent analytical, development, and debugging skills
Desirable Skills and Aptitudes
- Experience with GPUs, machine learning accelerators and related software is a plus
- Knowledge of ML compilers like TVM, GLOW, XLA is a plus
- Hands on use of version control tools like GIT, GITHUB
Qualifications
Bachelor's / Masters/ PHD degree in Engineering, Machine learning/ AI, Information Systems, Computer Science, or related field.
- 10 + years of Industry Experience in Systems engineering
- 5+ years of hand-on experience with machine learning workloads and tasks including computer vision, natural language processing and large language models
Minimum Qualifications:
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 6+ years of Systems Engineering or related work experience
- OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 5+ years of Systems Engineering or related work experience
- OR PhD in Engineering, Information Systems, Computer Science, or related field and 4+ years of Systems Engineering or related work experience.
