3 дня назад
Systems Developer and Researcher (AI/LLM)
236 000 - 330 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Systems Developer and Researcher (AI/LLM): Developing high-performance LLM inference systems and optimization techniques with an accent on distributed serving, GPU kernel optimization, and model-system co-design. Focus on building intelligent, adaptive inference systems that automate performance tuning and push the boundaries of latency, throughput, and scalability.
Location: Bellevue, WA, USA
Compensation: $236,000 – $330,000 per year
Company
Snowflake is a cloud-based data platform company powering the era of the agentic enterprise through AI-native engineering.
What you will do
- Design and develop high-performance LLM inference systems, including distributed serving and runtime systems.
- Develop novel techniques to improve inference latency, throughput, memory efficiency, and cost.
- Explore advanced inference techniques such as speculative decoding, KV-cache management, and adaptive parallelism.
- Apply AI-native approaches to systems engineering, including automated profiling and bottleneck identification.
- Develop strategies for multi-model serving, dynamic resource management, and model-system co-design.
- Collaborate with research, infrastructure, and product teams to deploy innovations into production.
Requirements
- Bachelor’s degree in Computer Science, Electrical Engineering, or related field (Master’s or PhD preferred).
- 5+ years of experience in LLM inference systems, distributed AI systems, GPU systems, or HPC.
- Strong understanding of modern LLM inference architectures and performance tradeoffs.
- Hands-on experience with frameworks like vLLM, SGLang, or TensorRT-LLM.
- Proficiency in GPU programming environments such as CUDA or Triton.
- Experience profiling system performance using tools like Nsight Systems or Nsight Compute.
Nice to have
- Experience using AI-native engineering approaches to accelerate software development and debugging.
- Experience with performance libraries like CUTLASS, cuBLAS, or cuDNN.
Culture & Benefits
- Opportunity to collaborate with world-class researchers and engineers from teams like DeepSpeed and vLLM.
- Focus on experimental mindset and rapid testing of emerging capabilities.
- Environment that encourages open-sourcing and publishing innovations in top-tier conferences.
- Commitment to redefining the future of work through AI-native engineering.
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