Cloud ML - Lead Engineer

14 Hours ago • 4-10 Years

Research Development

Job Description

The Qualcomm Cloud AI team is developing hardware and software for Machine Learning solutions spanning the data center, edge, infrastructure, and automotive market. We are seeking ambitious, bright, and innovative engineers with experience in machine learning framework development. The primary role of the engineer will be to train Large Language Models (LLMs) from scratch and fine-tune existing LLMs on various datasets using state-of-the-art techniques. This role involves optimizing LLM architectures for NPUs, implementing advanced training methods, managing large datasets, and evaluating model performance.

Good To Have:

Familiarity with data version control tools.
Hands-on experience with Exploratory Data Analysis (EDA).
Knowledge of Quantization (AWQ) and Quantization Aware Training (QAT).
Knowledge of optimization techniques for training large models.
Experience with Neural Architecture Search (NAS) techniques for optimizing model architectures.
Hands-on experience with CUDA, CUDNN, and Triton-lang.

Must Have:

Hands-on experience with PyTorch at a granular level, including tensor operations, automatic differentiation, and GPU acceleration.
Strong understanding of Natural Language Processing (NLP) and experience working with Large Language Models (LLMs).
Proficiency in Python and experience with software development best practices.
Experience working with large datasets, ensuring data quality and integrity, and implementing data cleaning and preprocessing techniques.
A degree in Computer Science, Machine Learning, AI, or a related field.
Excellent written and verbal communication skills.
Experience in model architecture optimizations, including reimplementing basic building blocks of models for NPUs.
Ability to implement state-of-the-art LLM training techniques such as Reinforcement Learning from Human Feedback (RLHF), ZeRO, and Speculative Sampling.
Sound understanding of various LLM metrics like MMLU, Rouge, BLEU, and Perplexity.
Minimum of 3+ years of Software Engineering or related work experience with a Bachelor's degree, or 2+ years with a Master's, or 1+ year with a PhD.
2+ years of academic or work experience with programming languages such as C, C++, Java, or Python.

Perks:

World-class health benefit options providing comprehensive coverage to employees and their eligible dependents.
Programs designed to help employees build and prepare for a financially secure future.
Self and family resources to build emotional/mental strength and resilience and define purpose.
Wellbeing programs and resources to support employees in living and working well, unlocking full potential.
Access to continuous learning and development programs.
Tuition reimbursement.
Mentorship opportunities.

Add these skills to join the top 1% applicants for this job

cross-functional

communication

data-analytics

cpp

game-texts

quality-control

cuda

pytorch

deep-learning

reinforcement-learning

python

java

machine-learning

Job Posting Date

2025-09-30

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Job Area:

Engineering Group, Engineering Group > Software Engineering

General Summary:

Job Overview:

The Qualcomm Cloud AI team is developing hardware and software for Machine Learning solutions spanning the data center, edge, infrastructure, automotive market. We are seeking ambitious, bright, and innovative engineers with experience in machine learning framework development. Job activities span the whole product life cycle from early design to commercial deployment. The environment is fast-paced and requires cross-functional interaction daily so good communication, planning and execution skills are a must.

We are seeking a highly skilled and motivated Language Model Engineer to join our team. The primary role of the engineer will be to train Large Language Models (LLMs) from scratch and fine-tune existing LLMs on various datasets using state-of-the-art techniques.

Responsibilities:

Model architecture optimizations : optimize latest LLM and GenAI model architectures for NPUs, which involves reimplementing basic building blocks of models for NPUs

Model Training and Fine-tuning:

Fine-tune pre-trained models on specific tasks or datasets to improve performance. Implement state-of-the-art LLM training techniques such as Reinforcement Learning from Human Feedback (RLHF), ZeRO (Zero Redundancy Optimizer), Speculative Sampling, and other speculative techniques.

Data Management:

Handle large datasets effectively. Ensure data quality and integrity. Implement data cleaning and preprocessing techniques. Hands-on with EDA is a plus.

Model Evaluation:

Evaluate model performance using appropriate metrics. Understand the trade-offs between different evaluation metrics.

LLM metrics: Sound understanding of various LLM metrics like MMLU, Rouge, BLEU, Perplexity etc.

AWQ: Understanding of Quantization is a plus. Knowledge on QAT will be a plus.

Research and Development:

Stay up to date with the latest research in NLP and LLMs. Implement state-of-the-art techniques and contribute to research efforts.

Infrastructure development : For coming up with new optimization techniques to minimize ONNX memory footprint, export time optimizations.

Collaboration:

Work closely with other teams to understand requirements and implement solutions.

Required Skills and Experience:

Deep Learning Frameworks:

Hands-on experience with PyTorch at a granular level. Familiarity with tensor operations, automatic differentiation, and GPU acceleration in PyTorch.

NLP and LLMs:

Strong understanding of Natural Language Processing (NLP) and experience working with LLMs.

Programming:

Proficiency in Python and experience with software development best practices.

Data Handling:

Experience working with large datasets. Familiarity with data version control tools is a plus.

Education:

A degree in Computer Science, Machine Learning, AI, or related field. Advanced degree is a plus.

Communication:

Excellent written and verbal communication skills.

Work experience : Open, 4 – 10 years of relevant experience.

Preferred Skills:

Optimization:

Knowledge of optimization techniques for training large models.

Neural Architecture Search (NAS):

Experience with NAS techniques for optimizing model architectures is a plus.

Hands-on experience with CUDA, CUDNN and Triton-lang is a plus.

Minimum Qualifications:

Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.

Master's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.

PhD in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience.

2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc.

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