ML Ops / Dev Ops Engineer

Zensors•San Francisco, CA

About The Position

Zensors is the spatial intelligence platform for the physical world. Our AI platform provides real-time insights—from airport queue times to office utilization—helping organizations make smarter operational decisions. Zensors processes massive streams of video data 24/7 with human-level accuracy. To do this at scale, we rely on cutting-edge optimization to ensure our vision transformers and spatial models run efficiently on both cloud and edge compute resources. Learn more at www.zensors.com. About the Role As an ML / DevOps Engineer, you will play a pivotal role in advancing our infrastructure, scaling enterprise deployment workflows, and refining automation architectures to enable rapid iteration across the organization. You will sit at the critical intersection of machine learning and systems engineering. This role requires deep technical expertise not just in cloud-native tools, but also in the foundational Linux systems and networking required to process high-throughput video data reliably and securely across both cloud and edge environments.

Requirements

A BS, MS, or PhD in Computer Science or a related equivalent field.
4+ years of applicable industry experience in DevOps, MLOps, or Systems Engineering.
You are a highly motivated professional with a strong track record of technical execution, complex systems integration, and successful cross-team collaboration.
Expert-level knowledge of Linux administration, kernel tuning, and system performance debugging.
Strong understanding of networking protocols (TCP/IP, UDP, DNS, VPNs, firewalls) and container networking challenges (CNI, service mesh).
Proven experience managing infrastructure for video streaming (e.g., RTSP, HLS, WebRTC) or similarly high-throughput, real-time data pipelines.
Deep expertise in Kubernetes (managing clusters, Helm charts, orchestration) and a strong background in CI/CD toolchains (e.g., Jenkins, GitLab CI, ArgoCD).
Proficiency in IaC tools (e.g., Terraform, Ansible).
Experience working in NixOS environments, declarative package management, and virtualization environments is highly required.

Responsibilities

Drive the design and implementation of automated infrastructure deployment and validation workflows supporting our cutting-edge AI and computer vision initiatives.
Design, optimize, and manage the infrastructure specifically tailored for ingesting, processing, and analyzing real-time video streams at scale. You will ensure high throughput, low latency, and rock-solid reliability for critical CV workloads.
Maintain a strong systems foundation by managing high-performance Linux environments. You will architect and troubleshoot complex networking configurations (both cloud and edge) necessary for seamless video data transmission between physical cameras, processing nodes, and the cloud platform.
Create resilient automation pipelines, orchestrate complex Kubernetes-based environments, and ensure the seamless integration of diverse ML and software components.
Design sophisticated CI/CD pipelines. Your scope will include automating infrastructure provisioning (potentially bare-metal-to-Kubernetes bring-up), deploying microservices utilizing Helm, and integrating security scans and static code analysis tools into the workflow.
Build comprehensive monitoring systems and automated alerting mechanisms tailored specifically for intensive AI/video workloads. Diagnose and resolve complex build failures and production issues related to system resources or network bottlenecks.
Collaborate deeply with Machine Learning engineers to ensure validation readiness for new models, and take ownership of scaling enterprise deployment workflows across the entire organization.