Senior AI Researcher, On-Device LLM Efficiency

Qualcomm

Confirmed live today High trust

Quick summary

Work type: On-site
Location: San Diego, CA
Salary: $159,100–$238,700 / yr
Posted: 11 days ago
Freshness: Confirmed live today
Closes: Mar 29, 2027
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $226k

This role $199k

$145k $292k

below market most similar roles pay here above market

This role pays less than 73% of similar roles. Most pay $196,750–$254,750 — the blue band above. At the midpoint, this role pays about $199k versus about $226k 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 558 open roles on FindRole.

Listed pay typically runs $152,130–$222,500 across 534 roles with salary data.

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At a glance

TL;DR · Senior AI Researcher, On-Device LLM Efficiency

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The Senior AI Researcher, On-Device LLM Efficiency joins the Machine Learning Researcher team to conduct fundamental research creating innovative machine learning methodology. The role focuses on research and development regarding LLM inference efficiency algorithms, efficient model architecture design, and LLM training. The successful candidate will develop creative solutions that address practical challenges on devices, implementing and evaluating these solutions in both simulation and on-device environments. Key technical requirements include a strong background in deep learning and Transformers, along with proficiency in Python and PyTorch. The work specifically addresses the technical problem of LLM reasoning and inference acceleration, including efficient attention, KV cache compression, and on-device AI deployment for mobile, edge, and IOT products.

Skills

Python PyTorch Deep Learning Transformers LLM Machine Learning Inference Acceleration Model Architecture On-device AI KV Cache Compression Efficient Attention Simulation

What you'll do

Research and develop LLM inference efficiency algorithms and efficient model architectures.
Conduct research on LLM training methodologies to achieve beyond state-of-the-art performance.
Develop creative solutions that address practical challenges of running models on devices.
Implement and evaluate potential solutions in both simulation and on-device environments.
Optimize machine learning models, systems, platforms, and methods for mobile and edge products.
Research inference acceleration techniques such as efficient attention and KV cache compression.

What we're looking for

Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 2+ years of engineering work experience.
PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field.
6+ months of academic and/or work experience developing and/or optimizing machine learning models, systems, platforms, or methods.
4+ years of AI research experience.
Strong background in deep learning and Transformers.
Strong programming skills in Python and PyTorch.
Experience in LLM reasoning or inference acceleration research.
PhD in Computer Science, Electrical Engineering, or related field (preferred).
Experience in LLM efficiency research such as efficient attention, inference acceleration, or KV cache compression (preferred).
Experience in on-device AI deployment on mobile or edge devices (preferred).
Publishing research papers at top-tier AI/ML conferences as a lead author (preferred).

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