# ML Infrastructure Engineer

**Company:** [Maven Robotics](https://hotfix.jobs/companies/maven-robotics)
**Location:** San Francisco, CA
**Role:** DevOps / SRE
**Skills:** Python, Go, Rust, C++, Kubernetes, Ray, Zenml, Gpu Compute, Infrastructure As Code, Observability
**Posted:** 2026-06-22

> Own and scale ML infrastructure for robotics AI: backend services, GPU orchestration, storage, and internal developer platforms across cloud and on-prem.

## Job Description

## Responsibilities
- Own the architecture, implementation, reliability, and evolution of Maven's machine learning infrastructure.
- Build backend services and platforms for managing data, artifacts, jobs, logs, metadata, and compute resources across cloud and on-premise environments.
- Design scalable systems for workload orchestration, storage, observability, security, and infrastructure automation.
- Build intuitive internal tools and abstractions that make complex infrastructure easy for engineers to use.
- Lead technical and commercial discussions with cloud and ML compute providers, including capacity planning, performance, reliability, and cost.

## Requirements
- Significant experience designing, building, and operating production backend, distributed, or compute infrastructure.
- A track record of independently owning complex infrastructure projects from architecture through deployment and ongoing operation.
- Strong programming ability in Python, Go, Rust, C++, or a similar backend or systems language.
- Experience operating GPU compute infrastructure and orchestrating distributed workloads using Kubernetes, Ray, ZenML, or similar systems.
- Experience designing and operating storage systems, observability platforms, infrastructure-as-code, and secure access controls.
- Experience managing large-scale GPU fleets or hybrid cloud and on-premise compute environments.
- Experience building internal developer platforms, CLIs, SDKs, or other self-service infrastructure tools.
- Strong technical judgment, leadership, and communication skills, with the ability to drive decisions across teams and external partners.
- Self-starter attitude with the ability to identify priorities and deliver durable solutions in a fast-paced startup environment.

## Nice-to-Haves
- Familiarity with GPU architecture, accelerator-aware software design, and profiling compute-intensive workloads.
- Exposure to infrastructure supporting large-scale robot learning workloads, including policy training, simulation, and multimodal data pipelines.
- Familiarity with SOC 2 controls, security practices, and audit readiness.

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**Apply:** https://hotfix.jobs/jobs/74d85cba-e5ad-4943-ac80-48c21cf68679
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