Careers
Quick summary
- Work type
- On-site
- Location
- New York, NY
- Posted
- 54 days ago
- Nearby
- 99+ roles within 25 mi
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Salary context
How this pay compares to similar roles
This listing doesn't post a salary. Most similar roles pay $152,150–$208,800.
Based on 238 similar postings.
Employer
About Qualcomm
Qualcomm is a leading American semiconductor and telecommunications company based in San Diego, CA.
Qualcomm currently has 750 open roles on FindRole.
Listed pay typically runs $154,000–$231,000 across 430 roles with salary data.
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At a glance
TL;DR · Careers
Join our dynamic team as a Junior Machine Learning Compiler Engineer in New York City, where you will work closely with experienced professionals to extend and optimize training and runtime frameworks for machine learning models. Your daily tasks include developing optimized software tools that enable AI models on specific hardware, collaborating on the co-design of ML hardware and software, and applying advanced statistical techniques to enhance model efficiency. You should have at least one year of experience with ML frameworks like TensorFlow or PyTorch, proficiency in programming languages such as Python or C++, and a solid understanding of low-level system interactions. This role involves working within a product area that focuses on embedding AI solutions into scalable hardware systems, addressing complex business challenges through innovative software development.
What you'll do
- Applies Machine Learning knowledge to extend training or runtime frameworks and optimize model efficiency tools.
- Assists in the modeling and development of machine learning hardware for inference solutions.
- Develops optimized software enabling AI models on specific hardware features.
- Collaborates with team members for joint design and development of compiler tools.
- Develops, adapts, or prototypes ML algorithms, models, or frameworks aligned with product roadmap.
What we're looking for
- At least 1 year of experience with ML frameworks like TensorFlow and PyTorch.
- Experience in embedded system development for specific ML problem domains.
- Proficiency in programming languages suitable for machine learning, such as Python or C++.
- Knowledge of statistics and probability applied to ML problems.
- Familiarity with low-level OS-Hardware interactions on platforms like Linux and Android.
- Applies ML knowledge to extend training frameworks and optimize model efficiency tools.
- Assists in the co-design of ML hardware and software for inference solutions.
- Develops optimized software enabling AI models to utilize specific hardware features.
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