Software Engineer - Fleet Management

About the role

Verkada is transforming how organizations protect their people and places with an integrated, privacy-sensitive AI-powered platform that includes solutions for video security, access control, air quality sensors, alarms, intercoms, and visitor management. We've got serious momentum in the market: more than 30,000 customers (including 100+ of the Fortune 500), a $5.8B valuation , more than $1 billion in annualized bookings, and backing from CapitalG, Sequoia Capital, General Catalyst, Felicis Ventures, Next47 and more. Physical AI is one of the most consequential technology shifts of our time, and Verkada is at the center of it. You can look at all kinds of communities to see our platform's impact in the world. It's the retailer that uses our agentic AI to deter theft before it happens. The warehouse that uses AI-powered alerts to make sure its team is protected on the floor with proper PPE. The school that's alerted to a threat in real-time and triggers a lockdown in seconds, not minutes. We're rapidly scaling this impact: today, more than 2 million Verkada devices are deployed across 170+ countries. We're looking for a backend software engineer with strong data analysis skills to join our camera fleet management team. You'll build the data infrastructure and analytical tools that power our safe release operations across a million+ camera devices. This role combines traditional backend engineering with data pipeline development, log analysis, and metrics-driven insights. Camera firmware releases include critical updates like new AI models, and understanding their impact requires sophisticated data analysis at scale. You'll develop the pipelines, dashboards, and analytical tools that help us detect anomalies, measure release health, and ensure every deployment is successful. Your work will directly support data-driven decision making for releases that impact our customers and our reputation. Every release decision we make affects hundreds of thousands of cameras in the field. The data pipelines you build and the insights you surface directly determine whether we can release confidently or need to halt a problematic rollout. You'll be the engineering force behind our data-driven release culture. You'll work closely with the Systems Software Engineer leading the team to build robust data infrastructure-from ingestion pipelines processing high-volume logs to SQL queries surfacing critical insights to real-time monitoring dashboards.

Responsibilities

• Build data pipelines : Design and implement data workflows using technologies like Kafka, Firehose, or Spark to process release metrics and device telemetry at scale
• Develop analytical tools : Create Python-based analysis tools using pandas and SQL to identify release issues, detect anomalies, and measure fleet health
• High-volume log analysis : Build systems to ingest, process, and analyze logs from millions of devices using technologies like OpenSearch, text clustering, and AI-based techniques
• Create monitoring infrastructure : Develop Grafana dashboards and alerts that surface critical metrics and anomalies in real-time
• Support release operations : Provide data-driven insights during releases, helping the team make informed decisions about rollout speed and risk
• Design test infrastructure : Build test bench setups and CI pipelines that validate releases before they reach production
• Query and optimize : Write efficient SQL queries against timeseries databases to extract insights from large-scale device data

Requirements

• BS/MS in Computer Science (or similar degree).
• 3+ years experience of industry experience in distributed software engineering.
• Strong Python skills : Proficiency in Python for data analysis, particularly with libraries like pandas
• SQL expertise : Experience writing complex SQL queries and queries for time-series analysis
• Backend engineering fundamentals : Solid software engineering skills-this is a backend role that happens to involve data, not a pure data engineering position
• Data pipeline experience : Familiarity with pipeline technologies like Kafka, Firehose, or Spark
• Log analysis at scale : Experience with high-volume log analysis technologies such as OpenSearch, text clustering, or AI-based log analysis techniques
• Timeseries databases : Experience working with timeseries databases and temporal data
• Metrics & observability : Hands-on experience with Grafana or similar monitoring tools
• Anomaly detection : Understanding of anomaly detection techniques and their practical application
• Coding-based analysis : Preference for solving problems through code rather than manual analysis
• Must be willing and able to work onsite five days per week.
• Experience with Go
• Background in statistics or experimental design
• Familiarity with A/B testing and statistical inference
• Experience with CI/CD systems
• Knowledge of test automation frameworks
• Understanding of distributed systems
• US Employee Be

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