Senior/Staff AI Engineer

🕒 Maio 15

🏄 California – Remoto

🏄 California – Remote

💵 $150.000 - $250.000 / ano

⏰ Tempo Integral

🟠 Sênior

🤖 Engenheiro de IA

🦅 Patrocina Visto H1B

Esta empresa já patrocinou vistos H1B no passado.

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DDN

SiteLinkedInTodas as Vagas

1001 - 5000 funcionários

Fundada em 1998

🤖 Inteligência Artificial

💰 $10.000.000 Funding Round em 2011-06

Artificial Intelligence • Data Center and Cloud Computing • High Performance Computing

DDN é líder global em soluções de inteligência de dados para IA, fornecendo tecnologias de computação de alto desempenho (HPC) e gestão sofisticada de dados. Com foco em acelerar implantações de IA e análises avançadas de dados, os produtos da DDN, incluindo a Data Intelligence Platform e sistemas de storage avançados, atendem a setores diversos como saúde, serviços financeiros e governo. A DDN está comprometida em transformar a infraestrutura de dados das empresas para aproveitar todo o potencial da IA e impulsionar a eficiência operacional.

Descrição

• Build and optimize LLM serving and inference systems for production environments • Improve performance across GPU and CPU pathways • Work on KV cache, memory, storage, and throughput bottlenecks • Design and scale systems that support RAG and retrieval-heavy AI workloads • Contribute to infrastructure where storage architecture and systems efficiency materially affect AI performance • Solve engineering problems at the intersection of AI, high-performance systems, and distributed infrastructure

🎯 Requisitos

• An engineer who has spent meaningful time building or optimizing production AI systems, not just experimenting with models • Someone who understands how inference performance is shaped by the interaction between compute, memory, storage, and serving architecture • Deep hands-on experience working close to the systems layer — for example, improving how workloads run across GPU and CPU resources, reducing bottlenecks, or tuning infrastructure for better throughput and latency • Evidence of real ownership in areas like model serving, retrieval, caching, storage, or distributed performance, rather than purely application-layer AI work • The ability to move comfortably between architecture decisions and hands-on implementation, especially in environments where efficiency and scale matter • A background that suggests you can operate in technically demanding environments, whether that comes from AI infrastructure, high-performance systems, storage platforms, or adjacent distributed systems work • PhD preferred, but far less important than having built serious systems in the real world.