Build and optimize the core edge platform for a hybrid edge-cloud video security system. Develop video streaming, recording, real-time AI processing, low-latency communications, performance profiling, and containerized deployments across resource-constrained devices. Collaborate with AI/ML engineers and perform code reviews, debugging, and performance tuning.
Description
The company is building an AI video security platform that doesn't just record — it understands. Founded in 2021, backed by tier-one Silicon Valley investors, and one of the fastest-growing companies in the video security space ($64M raised, 50k+ cameras deployed). Small teams, real ownership, direct impact.
The Senior Software Engineer, Edge joins a small, high-impact team building the software layer that runs on companies core device infrastructure. The system is a hybrid edge-cloud architecture spanning video, security, networking, storage, and on-device AI. Resource-constrained, performance-critical, real-time. You'll own how well the software behaves under load — CPU, memory, concurrency — on a dedicated machine where every cycle counts.
Responsibilities:
- Contribute to the core edge platform running across diverse hardware in a hybrid edge-cloud architecture
- Develop and integrate video management capabilities: streaming, recording, real-time AI processing
- Design and optimize for low-latency, high-performance workloads at the edge
- Build secure, efficient communication pipelines between edge devices and cloud systems
- Work with AI/ML engineers to deploy and optimize on-device models
- Own performance under load — profile CPU/memory, identify bottlenecks, fix them
- Participate in code reviews, debugging, and performance tuning across the stack
Must-Have Skills:
- Go / Rust / TypeScript (20%) — core stack; strong proficiency in at least one, ideally two
- Multithreaded & concurrent programming (18%) — critical requirement; proven experience with real parallel systems
- Performance profiling & optimization (15%) — CPU/memory management under real production load on dedicated hardware
- Docker & containerization (13%) — deploying and managing containerized workloads
- Edge computing / embedded systems (13%) — experience building for resource-constrained environments
- Networking & distributed systems (12%) — solid fundamentals; building reliable comms between edge and cloud
- Real-time systems (9%) — low-latency design and delivery
Nice-to-Have Skills:
- Video streaming / pipeline development
- Storage systems & data management at the edge
- Cloud services integration (AWS S3, GCP, MQTT, Kafka)
- AI/ML model deployment on edge devices
- Security concepts (encryption, secure communication, zero trust)
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