СШАAI Infrastructure Engineer, pAGI
1 мес. назад
266k–500k USD / годСШАSeniorHybrid
distributed systemsperformance optimizationgpucompute schedulinginference platforms
Engineer and operate infrastructure for large-scale AI model training and evaluation to improve reliability, efficiency, and throughput.
О компании
- pAGI Infra team builds and operates the systems that make large-scale model training and evaluation reliable, efficient, and easy to run. Our work spans distributed training infrastructure, inference and grading platforms, compute scheduling, and research tooling. We partner closely with researchers and engineering teams to turn new research needs into dependable infrastructure, improve GPU efficiency, and shorten the path from an experiment to a validated model.
- is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
- Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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- At , we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
Обязанности
- We’re looking for an AI Systems Engineer to help scale the infrastructure behind our training and evaluation workflows. You’ll own projects from identifying bottlenecks and designing solutions through deployment and operation. The work combines distributed systems engineering, performance optimization, and close collaboration with researchers. You might build a shared grading service, improve resource allocation across workloads, or bring a new training stack into production — directly improving how quickly and reliably research moves forward.
- Build and operate infrastructure for large-scale training and evaluation, improving reliability, throughput, and resource efficiency.
- Develop shared inference and grading platforms with automated capacity management, health monitoring, and visibility into performance.
- Improve compute scheduling and resource allocation to reduce idle GPU time and help workloads recover quickly from failures.
- Diagnose bottlenecks across training, inference, and orchestration, and work across teams to improve end-to-end performance.
- Build self-service tools, automated validation, and observability that help researchers launch experiments, diagnose issues, and compare results with less manual intervention.
- Are excited about the potential of personal AGI and want to build the infrastructure that enables it.
- Have strong software engineering fundamentals and experience building or operating large-scale distributed systems.
- Have experience in ML infrastructure, inference systems, GPU performance, or infrastructure tooling.
- Are highly self-motivated and comfortable taking ownership of open-ended problems.
- Enjoy debugging across system boundaries and using measurements to guide improvements in performance and reliability.