Build and optimize the core edge platform for a hybrid edge-cloud video security system. Develop video streaming/recording, low-latency real-time AI processing, secure edge-cloud communication, and on-device model deployments. Profile and fix CPU/memory bottlenecks, work on multithreaded concurrent systems, containerize workloads, and collaborate on performance tuning and code reviews.
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 Lumana's 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
- 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
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