Staff Machine Learning Engineer
Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Software Engineering
In short
Qualcomm is seeking a Staff Machine Learning Engineer to develop tools for optimizing and deploying ML models on edge and mobile hardware using the AIMET framework. This role involves designing quantization algorithms, implementing advanced techniques for LLMs and generative AI, and integrating with ML frameworks like PyTorch and ONNX to meet accuracy, latency, and power targets.
Responsibilities
- Design, develop, and maintain quantization algorithms and compression pipelines within the AIMET framework (PTQ, QAT, mixed-precision, AdaScale etc.)
- Implement advanced quantization techniques including weight-only quantization, activation quantization, KV-cache quantization, and sub-4-bit quantization for LLMs and generative AI models
- Build tooling to analyze, profile, and debug model accuracy degradation caused by quantization
- Integrate AIMET workflows with popular ML frameworks — PyTorch and ONNX
- Develop APIs and developer-facing tooling to make AIMET accessible and easy to use for external customers and design partners
- Integrate AIMET to enable Quantization at large scale.
- Own end-to-end quantization and optimization of models, ensuring they meet accuracy, latency, and power targets on Qualcomm hardware
- Quantize and validate a broad range of model families — vision transformers, LLMs, diffusion models, speech, and multimodal architectures
- Develop and maintain automated quantization pipelines and evaluation harnesses to scale model onboarding
Requirements
- Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
- Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
- PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
- 3+ years of industry experience in machine learning, deep learning, or AI infrastructure
- Strong proficiency in Python, with hands-on experience in PyTorch, ONNX and/or TensorFlow
- Solid understanding of neural network architectures — CNNs, Transformers, LLMs, diffusion models, multimodal models
- Experience with model quantization techniques — PTQ, QAT, weight-only quantization, mixed-precision, sub-4-bit methods
- Hands-on experience quantizing LLMs (GPT, LLaMA, Mistral, Falcon, or similar families) for inference optimization
- Familiarity with AIMET, GPTQ, AWQ, SmoothQuant, or similar quantization frameworks is a strong plus
- Experience working with ONNX, TFLite/LiteRT, or other model interchange formats
- Understanding of hardware constraints: memory bandwidth, compute precision (INT4/INT8/FP16/BF16), and NPU/DSP execution
- Experience collaborating across teams or BUs to drive technical alignment and model delivery
- Proficiency with git and software development best practices
- Strong written and verbal communication skills — ability to write clean APIs, documentation, and engage directly with external developers
- Experience with C++ for performance-critical components is a bonus
- Familiarity with ARM processors and mobile SoC architecture (Snapdragon) is a plus
- Experience with automated evaluation pipelines and model benchmarking at scale is a plus.
- 2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.
Desired Qualifications
- Provides technical guidance and mentorship to other team members
- Decision-making is significant and affects work beyond the immediate team
- Requires strong communication skills to convey complex quantization concepts to varied audiences — from hardware engineers and BU partners to external researchers and application developers
- Has meaningful influence on the AIMET product roadmap and cross-BU quantization strategy
- Tasks are open-ended; planning, prioritization, and problem-solving are core to the role
Benefits
- Qualcomm is an equal opportunity employer.
- Qualcomm is committed to providing an accessible process.
- Qualcomm is committed to making our workplace accessible for individuals with disabilities.
#Machine Learning#AI#Edge Devices#Model Optimization#Quantization#Compression#LLMs#Generative AI#Deep Learning#PyTorch#ONNX
Company
Qualcomm
Job Posted
1 day ago
Employment Type
Full Time
WorkMode
On Site
Experience Level
Senior
Locations
China
Qualification
Bachelor, Master, Doctoral
Applicants
Be an early applicant