Dev / ML Ops Engineer

Location: San Francisco, Pittsburgh

Location Type: IN_OFFICE

Employment Type: FULL_TIME

About the Role

Key Responsibilities

• Infrastructure & Automation Strategy: Drive the design and implementation of automated infrastructure deployment and validation workflows supporting our cutting-edge AI and computer vision initiatives.
• Video Pipeline Operations: 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.
• Systems & Networking Core: 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.
• Kubernetes & Orchestration: Create resilient automation pipelines, orchestrate complex Kubernetes-based environments, and ensure the seamless integration of diverse ML and software components.
• CI/CD & Deployment: 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.
• Reliability & Monitoring: 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.
• Collaboration & Scaling: 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.

Ideal Background & Qualifications

• Education: A BS, MS, or PhD in Computer Science or a related equivalent field.
• Experience: 6+ years of applicable industry experience in DevOps, MLOps, or Systems Engineering.
• Professional Profile: You are a highly motivated professional with a strong track record of technical execution, complex systems integration, and successful cross-team collaboration.
• Systems & Networking Mastery: 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).
• Data & Video Pipelines: Proven experience managing infrastructure for video streaming (e.g., RTSP, HLS, WebRTC) or similarly high-throughput, real-time data pipelines.
• Cloud-Native & CI/CD: Deep expertise in Kubernetes (managing clusters, Helm charts, orchestration) and a strong background in CI/CD toolchains (e.g., Jenkins, GitLab CI, ArgoCD).
• Infrastructure as Code: Proficiency in IaC tools (e.g., Terraform, Ansible).
• Specialized Environments: Experience working in NixOS environments, declarative package management, and virtualization environments is highly required.

What We Offer

• Competitive base salary + equity options.
• Comprehensive health, dental, and vision benefits.
• The rare opportunity to build the infrastructural backbone for a pioneering platform in Physical AI and computer vision.