Job Description
A Machine Learning Engineer role focused on developing AI-driven GPU kernel optimizations to enhance performance and reduce inference latency for self-driving technology applications.
Key Responsibilities
- Implement AI-driven methods for GPU kernel optimizations to improve efficiency and performance.
- Develop strategies for resource-efficient deployment of AI optimization processes using frameworks like AutoFDO.
- Guide high-level optimizations, including neural architecture search (NAS), to enhance training efficiency and inference latency.
- Assess performance improvements through evaluation metrics and real-world feedback.
- Collaborate with internal teams to benchmark optimization processes and strategies.
- Utilize leaderboard scoring systems to evaluate AI models' ability to generate efficient GPU kernels.
Requirements
- Bachelor's or Master's Degree in Computer Science, Engineering, or a related field.
- Extensive experience with AI models, including but not limited to Large Language Models (LLMs).
- Strong understanding of retrieval-augmented generation (RAG) and Language Models (LLMs).
- Ability to self-motivate, undertake complex assignments independently, and balance innovative exploration with practical considerations.
Benefits & Perks
Compensation/salary range between $193,930 and $352,290 depending on experience and qualifications
Annual performance bonus
Equity
Competitive benefits package
Ready to Apply?
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