Senior AI Research Quantization Engineer

Qualcomm

Confirmed live 2 days ago High trust

Quick summary

Work type: On-site
Location: San Diego, CA
Salary: $140,800–$211,200 / yr
Posted: 149 days ago
Freshness: Confirmed live 2 days ago
Closes: Oct 12, 2026
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $204k

This role $176k

$128k most similar roles pay here $263k

This role pays less than 73% of similar roles. Most pay $172,000–$235,750 — the shaded band above. At the midpoint, this role pays about $176k versus about $204k for comparable roles.

Based on 240 similar postings.

Employer

About Qualcomm

Qualcomm is a leading American semiconductor and telecommunications company based in San Diego, CA.

Qualcomm currently has 623 open roles on FindRole.

Listed pay typically runs $148,300–$222,500 across 603 roles with salary data.

Most-posted roles

View all roles at Qualcomm

At a glance

TL;DR · Senior AI Research Quantization Engineer

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As a Senior AI Research Quantization Engineer within the Machine Learning Engineering team, you will contribute to advanced machine learning technology and model optimization tools designed to run state-of-the-art networks on devices with limited power, memory, and computation. Your daily responsibilities involve research and development of algorithms for efficient generative AI, LLM, LVM, Multi-modal, and VLA models. You will work on inference algorithms like batching and KV caching, advanced quantization techniques including automatic mixed precision, and model compression through structural and neural search. To succeed in this role, you must be proficient in Python and Pytorch programming. The core of your work involves solving the technical challenge of optimizing complex generative models for deployment across various platforms, ranging from mobile devices to autonomous vehicles and robotics systems.

Skills

Generative AI LLM LVM Multi-modal VLA Python PyTorch Quantization Model Compression Deep Learning Machine Learning Systems Engineering Hardware Engineering

What you'll do

Research and develop algorithms for efficient generative AI, LLMs, LVMs, and multi-modal models.
Develop inference optimizations such as batching, KV caching, efficient attention, and speculative decoding.
Create advanced quantization algorithms for complex generative models using gradient or non-gradient based optimization.
Perform model compression through lossy/lossless methods and structural or neural searches.
Prototype systems for generative AI applications.
Implement model efficiency solutions for both on-device hardware and cloud environments.
Develop machine learning solutions using Python and PyTorch.

What we're looking for

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.
Master's degree in Computer Science, Engineering, Information Systems, or a related field.
PhD in Computer Science, Engineering, Information Systems, or a related field.
2+ years of experience in Hardware Engineering, Software Engineering, Systems Engineering, or related fields for Bachelor's degree holders.
1+ year of experience in Hardware Engineering, Software Engineering, Systems Engineering, or related fields for Master's degree holders.
2+ years of experience with Machine Learning algorithms or systems engineering.
Proficiency in Python and PyTorch programming.
Experience in research and development for generative AI, LLM, LVM, and model compression techniques.

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