Senior Backend Engineer - Cloud & Edge AI Pipelines NEW

The Briefed; San Mateo (CA); On-site; USD 180,000 - 280,000; Full time

Benefits offered by this job

RSUs

Health insurance

Parental leave

Wellness stipend

Lunch & commuter benefits

Professional development stipend

Flexible PTO

Visa sponsorship

Job summary

Verkada is transforming physical security with an integrated AI platform spanning video, access control, sensors, and more. As a Senior Backend Engineer, you will design and scale distributed cloud and edge infrastructure powering the products, building low-latency services that ingest multimodal streams and deploy ML models.

You will work with Go, Python, Kafka, Temporal, and Postgres to enable real-time decisioning, threat detection, and emergency dispatch across cloud and edge environments.

Qualifications

• 6+ years of professional software engineering experience building and operating distributed backend systems in production.

Responsibilities

• Distributed AI Backend Architecture: Design, build, and maintain low-latency backend microservices powering automated threat detection, real-time alert decisioning, and lockdown workflows.

Skills

Distributed Systems

AI Backend Engineering

Go

Python

Cloud Infra (AWS/GCP)

REST/gRPC APIs

Event-driven Architecture

Tools

Kafka

Temporal

Postgres

AWS

GCP

Job description

Verkada is transforming physical security with an integrated, privacy-sensitive AI platform spanning video security, access control, environmental sensors, alarms, intercoms, and visitor management. Operating across 30,000+ organizations with a $5.8B valuation, over 2 million deployed devices ingest, analyze, and act on physical data worldwide in real time.

As a Senior Backend Engineer, you will build and scale the distributed cloud and edge infrastructure powering Verkada's physical AI security products. You will design low-latency, high-throughput backend services that ingest multimodal video, audio, and sensor streams, coordinate directly with edge devices in the field, and deploy large vision and audio ML models. Working with technologies like Go, Python, Kafka, Temporal, and Postgres, you will architect real-time decisioning workflows that trigger automated threat detection, verification, and emergency dispatch.

Key Responsibilities

• Distributed AI Backend Architecture: Design, build, and maintain low-latency backend microservices powering automated threat detection, real-time alert decisioning, and lockdown workflows.
• Multimodal Data Pipelines: Develop event-driven pipelines for ingesting and processing high-volume video, audio, and sensor streams in real time across cloud backend environments and global edge devices.
• Cloud & Edge Device Coordination: Architect services that orchestrate directly with on-device AI workloads at the edge, balancing compute load between cloud pipelines and field hardware.
• ML Model Integration: Build production infrastructure to deploy, serve, and evaluate large vision and audio machine learning models for real-time video/audio analysis.
• System Performance & Reliability: Optimize backend architectures for high availability, fault tolerance, and sub-second latency using event-driven tools (Kafka, Temporal, Postgres).
• Observability & Prototyping: Rapidly prototype new AI-driven capabilities, establishing best practices for distributed tracing, automated testing, continuous integration, and observability.

Requirements

Distributed Systems & AI Backend Engineering

• 6+ years of professional software engineering experience building and operating distributed backend systems in production.
• Distributed Systems & Event Architectures: Proven experience designing scalable REST/gRPC APIs, event-driven microservices, and high-throughput real-time streaming architectures.
• Core Systems Languages: Strong proficiency in Go or Python with hands-on cloud infrastructure experience (AWS, GCP).
• Data & Streaming Infrastructure: Hands-on experience operating distributed databases, message queues, and workflow orchestration engines (Kafka, Temporal, Postgres).
• ML Model Integration: Familiarity with integrating or deploying machine learning models in production, particularly within video, audio, or multimodal data domains.
• Execution & Ambiguity: Self-directed with a strong ownership mindset, capable of prototyping rapidly and shipping production-grade systems in fast-moving environments.

Preferred Qualifications

• Experience building software systems that interact directly with IoT devices, edge compute nodes, embedded cameras, or hardware sensors.
• Hands-on experience with real-time video streaming protocols (RTSP, WebRTC) or media processing pipelines (FFmpeg, GStreamer).
• Track record of building zero-trust, security-conscious backend architectures handling sensitive video/audio streams.

Total Rewards & Benefits

• Compensation & Equity: Estimated pay range of $180,000 - $280,000 USD + Restricted Stock Units (RSUs) + performance incentives.
• Comprehensive Healthcare: 100% employer-paid premiums for employees (80% for dependents) across medical, dental, and vision plans + HSA employer contributions + FSA.
• Family & Wellness: Paid parental leave, fertility stipends/benefits, mental health coaching, and wellness/fitness stipends.
• Perks & Stipends: Daily catered lunches, commuter benefits, professional development stipends, flexible PTO, and paid firmwide extended holidays.
• Visa Sponsorship: Full employment visa sponsorship provided (H-1B transfer, O-1, green card processing).