AI that can watch your business to create better customer experiences. Backed by Y Combinator.
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.
What they're looking for
- Education: A BS, MS, or PhD in Computer Science or a related equivalent field
- Experience: 4+ 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
- 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)
More about this role
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 .
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.
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