The role:

Build the control plane and data plane for physical AI.

Munari must run training, simulation, evaluation, data processing, and inference workloads across public cloud, customer-owned infrastructure, robotics labs, and fleets of edge devices. It must handle GPUs, enormous multimodal datasets, unreliable connectivity, long-running workloads, and machines that cannot simply be restarted whenever something goes wrong.

This is not a conventional DevOps role and it is not a YAML-only role. You will write the systems software that makes physical AI infrastructure feel closer to a programmable platform than a pile of bespoke operations.

What you will work on:

• 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-prem environments, and edge devices.

• Build reliable systems for checkpointing, retrying, resuming, scaling, and observing long-running robotics workloads.

• Create the 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 the APIs, SDKs, CLIs, and deployment workflows that make the underlying infrastructure simple 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.

You may be a strong fit if:

• You have built or operated large-scale distributed systems where reliability genuinely mattered.

• You have strong experience with Kubernetes, infrastructure as code, networking, storage, security, and cloud architecture.

• You understand GPU infrastructure, distributed training, batch systems, MLOps, or high-performance computing.

• You have worked with high-volume streaming, time-series, image, video, or scientific data.

• You write production software in Rust, Go, Python, or a similar systems language rather than treating infrastructure as configuration alone.

• You are comfortable debugging across an application, container, scheduler, network, host, GPU, and storage system.

• You care equally about the internal architecture and the developer experience exposed to the customer.

Experience with NVIDIA infrastructure, Slurm, Ray, Kueue, Temporal, Kafka, NATS, ClickHouse, Parquet, object storage, multi-cluster Kubernetes, or edge fleet management is useful but not mandatory.