
Graphics SW Engineer (Applied and Distributed ML)
The opportunity involves performing measurements and analysis of (distributed) machine learning workloads and developing optimizations to improve training and inference speed of (large) ML models used in production. We offer the unique opportunity to work across all layers of the hardware and software stack. Opportunities exist for applying and/or optimizing DL/ML models that improve the quality and/or performance of a rasterizing or ray-tracing based graphics pipeline. Implementing state-of-the-art machine learning models including those that scale across nodes. Profiling ML workloads on a distributed GPGPU cluster. Analyzing bottlenecks and performance trade-offs. Developing optimizations to speed up training and inference for a variety of models.
1+ years educational or work experience
Experience in Data parallelism, model parallelism and/or hybrid parallelism for training of large models and large data sets
Experience in systems-level optimization of distributed systems (e.g., data movement, network protocols, task schedulers)
Python
C++
Data Analysis
Pytorch
Time Management Skills
Natural language processing skills
Graphics processing
Verbal communication
written communication
Self-Driven
Adaptability
Problem-Solving
Detail Oriented and Organized
Multi tasker
According to JobzMall, the average salary range for a Graphics SW Engineer (Applied and Distributed ML) in 1300 S MoPac Expy, Austin, TX 78746, USA is between $96,745 and $144,664. This is based on the salaries reported by employers on the JobzMall platform.
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Intel Corp. engages in the design, manufacture, and sale of computer products and technologies. It delivers computer, networking, and communications platforms. It operates its business through the following segments: Client Computing Group, Data Center Group, Internet of Things Group, Non-Volatile Memory Solutions Group, Intel Security Group, and Programmable Solutions.

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