Senior AI Compute Engineer – HPC, GPUs & Distributed Training

Company:Qualcomm India Private Limited
Job Area:Engineering Group, Engineering Group > Hardware Engineering

General Summary:

We are seeking a Senior AI Compute Engineer to develop, optimize, and scale compute infrastructure for training and deploying ML and GenAI workloads. This role focuses on GPU acceleration, distributed training frameworks, and high‑performance compute systems

Minimum Qualifications:

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

Key Responsibilities

• Design and optimize GPU‑accelerated training pipelines for ML and LLM workloads.

  
  
  

• Implement distributed training strategies using frameworks like PyTorch Distributed or DeepSpeed.

  
  
  

• Work with HPC clusters, multi‑GPU systems, and parallel computing architectures.

  
  
  

• Profile, optimize, and troubleshoot compute performance bottlenecks.

  
  
  

• Collaborate with ML and platform teams to integrate scalable compute solutions.

  
  
  

• Develop tools for monitoring, scheduling, and managing large‑scale training jobs.

  
  
  

• Optimize CUDA kernels, memory usage, and compute flows where needed.

  
  
  

Minimum Qualifications:

• Bachelor’s or Master’s in Computer Science, Computational Engineering, or similar.

  
  
  

• Strong expertise in GPU computing, CUDA, or parallel processing.

  
  
  

• 3–8 years of experience working with ML model training environments.

  
  
  

• Hands‑on experience with distributed training frameworks.

  
  
  

• Solid understanding of computer architecture and performance optimization.

  
  
  

• Strong analytical and problem-solving skills.

  
  
  

• Hands-on experience with supervised and unsupervised learning techniques (e.g., classification, clustering, dimensionality reduction).

  
  
  

• Experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch

  
  
  

Preferred Qualifications:

• Experience with multi‑node training, HPC clusters, or cloud GPU environments.

  
  
  

• Experience in large Model Development & Training from the Scratch.

  
  
  

• Familiarity with model parallelism, pipeline parallelism, or large‑scale DL training.

  
  
  

• Experience with deep neural network architectures including RNNs, and Transformers.

  
  
  

• GenAI, LLMs, RAG Optimization. LLM Finetuning, Distillation Experience.

  
  
  

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