---
title: 'Dev / ML Ops Engineer at Zensors'
canonical: 'https://feeny.ai/job/dev-ml-ops-engineer-zensors-san-francisco-a2y0nx97nf0f'
type: 'job'
last_seen: '2026-09-11'
---

# Dev / ML Ops Engineer at Zensors

- **Company:** Zensors
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-05-11
- **Last confirmed live:** 2026-09-11
- **Apply:** https://jobs.gem.com/zensors-com/am9icG9zdDrCKpH7nViWxsbpnFwiXulg

## Job description

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](https://www.google.com/url?sa=E&q=http%3A%2F%2Fwww.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.

## 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.

## About Zensors

## Company Overview
- **One-liner**: Zensors is a physical AI platform that uses computer vision and spatial intelligence to help large physical businesses—like airports and retailers—automate operations and improve customer experiences.
- **Entity Type**: Private (Startup, Y Combinator S21)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2019
- **Founders**: Anuraag Jain (CEO)

## Core Business
- **Primary industry/industries**: Artificial Intelligence, Computer Vision, Physical AI, Airport & Retail Operations
- **Target customers**: B2B, Enterprise (airports, mass transit hubs, retail chains, large physical venues)
- **Mission or purpose statement**: "Let anyone create and manage AI without a Ph.D. in ML." — spun out of Carnegie Mellon University, the birthplace of AI.

## Products & Services
- **Zensors Physical AI Platform (Hologram)**: A multimodal AI that uses existing security cameras and sensors to understand people, assets, and spaces in real time. It provides real-time wait times, lane optimization, staff allocation, and AI agents that guide passengers. Deployed across airports and mass transit hubs.
- **Spatial AI**: Foundation models and spatial ontologies that transform video and sensor data into actionable intelligence.
- **Virtual Manager**: AI-driven operations automation for physical locations.
- **On-Prem Solutions**: Deployable on existing hardware for privacy-sensitive environments.

## Market Standing
- **Valuation/Market Cap**: Not publicly available (private company).
- **Key Metric**: Total Funding — $160K (across multiple rounds; latest Seed VC-III). Revenue is not publicly disclosed.
- **Notable Investors/Partners**: Y Combinator (S21), Bossa Invest, Tango.vc, SmartCityX, Plug and Play Japan, AcceliCITY. Key customer: Toronto Pearson Airport. Major partnership: Transportation Security Administration (TSA) — awarded a contract to provide Physical AI for real-time passenger throughput data at security checkpoints nationwide.
- **Growth Signals**:
  - Headcount: 15 employees (+14.3% YoY) as of mid-2026.
  - LinkedIn followers: 2,621 (+22.2% yearly).
  - Won a competitive TSA contract (announced June 2026) to standardize "TSA Real Time Wait Time" across US airports.
  - Deployed at Boston Logan International Airport (BOS) and Harry Reid International Airport (LAS).
  - Actively working with several other major airports.

## Competitive Advantages
- **Carnegie Mellon lineage**: Spun out of CMU, the birthplace of AI, giving deep technical roots in computer vision.
- **TSA "seal of approval"**: The only AI platform authorized to display the official "TSA Real Time Wait Time" mark — a significant barrier to entry for competitors.
- **Hardware-agnostic**: Works with existing security cameras and sensors, eliminating the need for costly new infrastructure.
- **Real-time, anonymous**: Maintains visitor anonymity while tracking operational metrics — critical for privacy-conscious clients.

## Strategic Focus
- **Scaling airport deployments**: Leveraging the TSA partnership to roll out "TSA Real Time Wait Time" across major US airports.
- **Expanding into new verticals**: Retail, real estate, and other large physical venues where spatial intelligence can optimize operations.
- **Product development**: Building out AI agents that dynamically guide passengers and automate physical-world workflows.

## Why Work Here
- **Mission-driven**: Work on AI that has tangible, real-world impact — reducing airport wait times, improving passenger experiences, and making physical spaces smarter.
- **Early-stage startup culture**: Small team (15 people) with flat hierarchy — high ownership and impact from day one.
- **Remote-friendly**: Several roles are listed as remote (e.g., Frontend Web Developer, Machine Learning Infrastructure Engineer).
- **Diverse, global team**: Workforce distributed across the US, India, and Pakistan.
- **Cutting-edge tech stack**: Work on foundation models, spatial ontologies, and real-time computer vision systems.
- **Open roles (as of mid-2026)**: Account Executive, Customer Success Lead, Product Marketing Lead, Solutions & Sales Operations, Frontend Web Developer (React/TypeScript), Machine Learning Infrastructure Engineer.

## Sources
1. [LinkedIn Company Page](https://linkedin.com/company/zensors)
2. [Zensors About Page](https://www.zensors.com/company/about-zensors-vision-ai)
3. [Zensors Careers Page](https://careers.zensors.com/jobs)
4. [CB Insights Profile](https://www.cbinsights.com/company/zensors)
5. [Y Combinator Profile](https://www.ycombinator.com/companies/zensors-inc)
6. [GlobeNewswire – TSA Partnership Announcement](https://www.globenewswire.com/news-release/2026/06/04/zensors-physical-ai-enables-tsas-new-standard-for-real-time-passenger-experience)

## Other roles at Zensors

- [Product Marketing Lead - Physical AI](https://feeny.ai/job/product-marketing-lead-physical-ai-zensors-san-francisco-p3wjdmz02tfe) — San Francisco, CA
- [Enterprise Account Executive](https://feeny.ai/job/enterprise-account-executive-zensors-san-francisco-kq1t24mzrve4) — San Francisco, CA
- [AI Solutions Specalist](https://feeny.ai/job/ai-solutions-specalist-zensors-san-francisco-er12xggx6z67) — San Francisco, CA