Infrastructure Engineer - Platform

WHO WE ARE:

Peripheral is developing spatial intelligence, starting in live sports and entertainment. Our models generate spatial data, used for advanced sports analytics and immersive media experiences. We’re solving key research challenges in 3D computer vision, creating the foundations for the next generation of robotic perception and embodied intelligence.

We’re backed by top investors, including Khosla Ventures, Inovia, Deloitte Ventures, Daybreak, and Entrepreneurs First, and working with some of the biggest names in sports. Our team includes engineers and researchers from leading technology companies and research institutions, and we’re building technology at the intersection of AI, graphics, and the future of live entertainment. We’re ambitious and looking to win.

We’re seeking an experienced Infrastructure Engineer to architect and build the cloud infrastructure that powers Peripheral, from large-scale foundation model training to reliable inference during live events.

You’ll work directly with our CTO and in collaboration with research and engineering teams to turn models and modules into scalable production systems. This is a highly architectural role that requires strong systems thinking and deep experience in Dev Ops and MLOps. You should understand how backend systems fail and evolve as usage grows and anticipate and design the architecture, processes and tooling to scale reliably.

You’ll help define how systems across Peripheral work together to deliver spatial experiences to customers. At the same time, you’ll build the infrastructure that keeps our research velocity high, including setting up and maintaining ML training clusters, distributed training infrastructure with PyTorch, and optimized multi-view video data loading.

The role is highly cross-functional and hands-on. You’ll build working knowledge across capture systems, robotics, foundation model training, and production inference to support teams across Peripheral while keeping customer-facing systems reliable. We’re also looking for someone who can mentor others as the team grows, maintain clear documentation in a fast-moving environment, and ship high-quality infrastructure with strong attention to detail.

You’ve built and scaled production cloud infrastructure and are comfortable owning systems end to end, from large-scale training workloads to reliable model serving.

You think in terms of systems, tradeoffs, and failure modes. Cost, latency, reliability, security, and developer productivity are all first-class concerns, and you know how to balance long-term architecture with pragmatic execution.

You collaborate well across research and engineering, communicate clearly, value strong documentation, and can provide technical leadership and mentorship as the infrastructure function grows.

You’re excited to help shape Peripheral’s infrastructure culture from the ground up, establishing the tooling, standards, and practices the broader engineering organization will build on as the company scales.

• Design, build, and operate cloud infrastructure for large-scale ML training, including GPU/TPU compute, job orchestration, and experiment tracking.

• Partner with research to scale foundation model training workflows, including distributed PyTorch training and efficient multi-view video data loading.

• Build and operate production infrastructure for model deployment and serving, with a focus on reliability, latency, scalability, and cost.

• Design reliable data pipelines for moving multi-view video, LiDAR, and other sensor data from capture through customer-facing outputs.

• Work across research and engineering to translate infrastructure needs into scalable, self-serve systems and developer tooling.

• Establish standards for infrastructure-as-code, CI/CD, monitoring, observability, security, and cloud operations.

• Improve the reliability and resilience of customer-facing systems during live, time-sensitive workloads.

• Mentor engineers and interns as the infrastructure function grows.

• Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, or a related field, or equivalent…