Founding Engineer (Physical AI Infrastructure)

a16z-speedrun; San Francisco (CA); On-site; USD 190,000 - 240,000; Full time

Job summary

a16z-speedrun is seeking an experienced systems engineer to build and operate the control and data planes for physical AI across public clouds, customer-owned infrastructure, robotics labs, and edge fleets. You will design orchestration across CPUs/GPUs, Kubernetes, bare metal, on-prem, and edge devices, while ensuring reliability for long-running workloads and complex data pipelines.

Role emphasizes production-grade Rust/Go/Python software, strong debugging across app, container, scheduler,

Qualifications

• Built or operated large-scale distributed systems with reliability.
• Strong experience with Kubernetes, IaC, networking, storage, security, and cloud architecture.
• Understanding of GPU infrastructure, distributed training, batch systems, MLOps, or HPC.
• Worked with high-volume streaming, time-series, image, video, or scientific data.
• Production software in Rust, Go, Python, or similar systems language.
• Able to debug across app, container, scheduler, network, host, GPU, and storage.
• Care about internal architecture and developer experience for customers.

Responsibilities

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

Skills

Kubernetes

IaC

Networking

Storage

Security

Cloud architecture

GPU infrastructure

MLOps

Distributed systems

Rust/Go/Python

Tools

Ray

Kueue

Temporal

Kafka

NATS

ClickHouse

Parquet

Job description

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.