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<p><strong>Role Overview</strong></p>
<p>You will be part of a team responsible for <strong>bringing a software stack to production for next-generation AI compute platforms</strong>. As a member of the <strong>Software QA team</strong>, you will design and build <strong>automated test frameworks</strong> for AI hardware and software components, and continuously enhance the <strong>test and qualification infrastructure</strong> to support evolving products.</p>
<p>This role involves leveraging <strong>CI/CD pipelines and Python-based test frameworks</strong> to drive both <strong>component-level and end-to-end validation</strong>. You will develop automation to support <strong>large language model accuracy, benchmarking, and robustness testing</strong>, ensuring software and hardware stacks meet high quality and performance standards.</p>
<p>You will collaborate closely with <strong>machine learning kernel teams, compiler and runtime engineers, hardware architects, and ML researchers</strong> across global teams throughout the qualification lifecycle. There will also be opportunities to contribute to <strong>emerging ML and AI technologies</strong> as part of the broader engineering effort.</p>
<p><strong>Qualifications</strong></p>
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<p>Bachelor’s or Master’s degree in <strong>Computer Science, Electrical Engineering, or a related field</strong>, with <strong>7+ years of industry experience</strong>.</p>
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<p>Solid understanding of <strong>machine learning fundamentals and large language models</strong>.</p>
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<p>Strong proficiency in <strong>Python</strong>, including data structures, and experience working in a <strong>Linux environment</strong>.</p>
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<p>Self-driven team player with strong collaboration skills.</p>
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<p><strong>Preferred Experience</strong></p>
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<p>Experience working in <strong>startups or small, fast-paced teams</strong>.</p>
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<p>Hands-on exposure to <strong>machine learning models and neural network layers</strong>.</p>
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<p>Experience testing <strong>deep learning frameworks</strong> such as <strong>TensorFlow, PyTorch, and transformer-based models</strong>.</p>
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<p>Familiarity with <strong>CI/CD systems</strong>, particularly GitLab-based pipelines.</p>
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