RecruitSeq
This range is provided by RecruitSeq. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range $200,000.00/yr - $600,000.00/yr
Member of Technical Staff, Machine Learning
Our client is a cutting-edge AI startup in the Bay Area developing highly efficient foundational models for real-world deployment across devices. Rapidly growing, highly technical team focused on building top-tier large language model (LLM) architectures with real-world impact.
As a Member of Technical Staff, you’ll drive innovation on large-scale model training, infrastructure, and optimization. You’ll collaborate closely with a small team of seasoned researchers and engineers, advancing state-of-the-art LLMs for efficient deployment at scale.
Responsibilities
Design, implement, and optimize large-scale pretraining and post-training pipelines for language models
Tackle challenges in model parallelism, distributed training, and low-level hardware/software co-design
Monitor, maintain, and troubleshoot massive training and inference workloads end-to-end
Collaborate on advancing core model architectures, inference optimizations, and custom hardware design
Contribute to open-source community initiatives and research publications
Analyze and streamline data pipelines, instruction data curation, and evaluation methods
Apply advanced optimization theory to improve model performance
Qualifications
Degree in Computer Science, Electrical Engineering, or related technical field (or equivalent practical experience)
Hands‑on experience in machine learning research centered on LLMs, efficient AI systems, or large‑scale model training
Strong proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
Expertise with distributed training, parallelization strategies, and large‑scale computational infrastructure
Understanding of low‑level GPU optimizations, CUDA, or similar technologies
Preferred Skills
Previous work at leading research labs or high‑impact contributions community AI projects
Experience with custom hardware, FPGA/ASIC design, or maximizing training throughput
Familiarity with open‑source inference engines (e.g., llama.cpp, vllm, triton)
Academic publications in optimization, LLM training, or AI infrastructure
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Engineering and Research
Industries: Staffing and Recruiting and Software Development
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Base pay range $200,000.00/yr - $600,000.00/yr
Member of Technical Staff, Machine Learning
Our client is a cutting-edge AI startup in the Bay Area developing highly efficient foundational models for real-world deployment across devices. Rapidly growing, highly technical team focused on building top-tier large language model (LLM) architectures with real-world impact.
As a Member of Technical Staff, you’ll drive innovation on large-scale model training, infrastructure, and optimization. You’ll collaborate closely with a small team of seasoned researchers and engineers, advancing state-of-the-art LLMs for efficient deployment at scale.
Responsibilities
Design, implement, and optimize large-scale pretraining and post-training pipelines for language models
Tackle challenges in model parallelism, distributed training, and low-level hardware/software co-design
Monitor, maintain, and troubleshoot massive training and inference workloads end-to-end
Collaborate on advancing core model architectures, inference optimizations, and custom hardware design
Contribute to open-source community initiatives and research publications
Analyze and streamline data pipelines, instruction data curation, and evaluation methods
Apply advanced optimization theory to improve model performance
Qualifications
Degree in Computer Science, Electrical Engineering, or related technical field (or equivalent practical experience)
Hands‑on experience in machine learning research centered on LLMs, efficient AI systems, or large‑scale model training
Strong proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
Expertise with distributed training, parallelization strategies, and large‑scale computational infrastructure
Understanding of low‑level GPU optimizations, CUDA, or similar technologies
Preferred Skills
Previous work at leading research labs or high‑impact contributions community AI projects
Experience with custom hardware, FPGA/ASIC design, or maximizing training throughput
Familiarity with open‑source inference engines (e.g., llama.cpp, vllm, triton)
Academic publications in optimization, LLM training, or AI infrastructure
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Engineering and Research
Industries: Staffing and Recruiting and Software Development
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