ML Ops / Dev Ops Engineer
Remote • San Francisco • FullTime
Posted 5mo ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Machine Learning Engineer
About the job
Zensors is seeking an ML / DevOps Engineer to advance its infrastructure, scale enterprise deployment workflows, and refine automation architectures. This role sits at the intersection of machine learning and systems engineering, requiring deep expertise in cloud-native tools, Linux systems, and networking for reliable and secure processing of high-throughput video data across cloud and edge environments.
Responsibilities
- Drive the design and implementation of automated infrastructure deployment and validation workflows for AI and computer vision initiatives.
- Design, optimize, and manage infrastructure for ingesting, processing, and analyzing real-time video streams at scale.
- Maintain and manage high-performance Linux environments and architect/troubleshoot complex networking configurations for video data transmission.
- Create resilient automation pipelines, orchestrate Kubernetes-based environments, and ensure seamless integration of ML and software components.
- Design CI/CD pipelines, automate infrastructure provisioning, deploy microservices using Helm, and integrate security scans.
- Build comprehensive monitoring systems and automated alerting mechanisms for AI/video workloads, and diagnose/resolve complex build and production issues.
- Collaborate with Machine Learning engineers to ensure model validation readiness and scale enterprise deployment workflows.
Requirements
- BS, MS, or PhD in Computer Science or a related equivalent field.
- 4+ years of applicable industry experience in DevOps, MLOps, or Systems Engineering.
- Highly motivated professional with a strong track record of technical execution, complex systems integration, and 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 CI/CD toolchains (e.g., Jenkins, GitLab CI, ArgoCD).
- Proficiency in Infrastructure as Code tools (e.g., Terraform, Ansible).
- Experience working in NixOS environments, declarative package management, and virtualization environments is highly required.
Benefits
- Competitive base salary
- Equity options
- Comprehensive health, dental, and vision benefits
- Opportunity to build the infrastructural backbone for a pioneering platform in Physical AI and computer vision.