Founding Engineer (Physical AI Infrastructure)

a16z-speedrun • San Francisco, CA

1d

About The Position

Build the control plane and data plane for physical AI infrastructure. Munari must run training, simulation, evaluation, data processing, and inference workloads across public cloud, customer-owned infrastructure, robotics labs, and fleets of edge devices. The platform must handle GPUs, large multimodal datasets, unreliable connectivity, long-running workloads, and machines that cannot simply be restarted when problems occur. This is a systems software role focused on making physical AI infrastructure a programmable platform rather than a collection of bespoke operations, not a conventional DevOps or YAML-only role.

The engineer will build a unified execution platform; design workload scheduling and orchestration across varied compute environments; create reliable systems for long-running robotics workloads; and develop data infrastructure for multimodal data. The role also includes building APIs, SDKs, CLIs, and deployment workflows; owning platform reliability, observability, capacity management, incident response, security, tenancy, and cost efficiency; supporting disconnected and private customer environments; and connecting cloud infrastructure with the Munari edge runtime.

Requirements

• Experience building or operating large-scale distributed systems where reliability mattered.
• Strong experience with Kubernetes, infrastructure as code, networking, storage, security, and cloud architecture.
• Ability to write production software in Rust, Go, Python, or a similar systems language.
• Comfort debugging across applications, containers, schedulers, networks, hosts, GPUs, and storage systems.
• Care about both internal architecture and the developer experience exposed to customers.

Nice To Haves

• Understanding of GPU infrastructure, distributed training, batch systems, MLOps, or high-performance computing.
• Experience with high-volume streaming, time-series, image, video, or scientific data.
• Experience with NVIDIA infrastructure, Slurm, Ray, Kueue, Temporal, Kafka, NATS, ClickHouse, Parquet, object storage, multi-cluster Kubernetes, or edge fleet management.

Responsibilities

• Build a unified execution platform for simulation, policy evaluation, model training, data processing, and deployment.
• Design workload scheduling and orchestration across CPUs, GPUs, Kubernetes clusters, bare metal, on-premises environments, and edge devices.
• Build reliable systems for checkpointing, retrying, resuming, scaling, and observing long-running robotics workloads.
• Create data infrastructure for video, audio, sensor streams, telemetry, trajectories, policy traces, incidents, and human interventions.
• Design storage, indexing, lineage, replay, retention, and movement of large multimodal datasets.
• Build APIs, SDKs, CLIs, and deployment workflows that simplify the infrastructure for robotics engineers and researchers.
• Own platform reliability, observability, capacity management, incident response, security, tenancy, and cost efficiency.
• Support disconnected, bandwidth-constrained, private, and potentially air-gapped customer environments.
• Connect cloud-side infrastructure to the Munari edge runtime and make the entire system operable as one platform.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed