Video Engineer

About the Role

As a Video Engineer at Naveera, you'll build and optimize the real-time camera and video infrastructure that powers our in-vehicle systems. You will work on low-latency multi-camera pipelines, hardware-accelerated encoding, stream reliability, and video performance across edge devices. This role is ideal for someone who understands how frames, buffers, latency, bandwidth, and system constraints interact in production, and who can build robust video systems that perform reliably without dropping frames.

Responsibilities

• Design, build, and optimize real-time camera and video pipelines for production systems
• Develop and maintain multi-stream video ingestion, processing, and encoding workflows
• Work with hardware-accelerated video encoding and decoding, including H.264 and H.265 pipelines
• Tune systems for low latency, stable throughput, and efficient bandwidth usage across edge devices
• Debug and resolve frame drops, sync issues, memory bottlenecks, buffering problems, and pipeline instability
• Integrate camera inputs, buffering logic, and encoder output into larger embedded and edge compute systems
• Profile and optimize CPU, GPU, memory, and I/O usage across video workloads
• Collaborate with embedded, backend, and product teams to ensure video systems are production-ready and operationally reliable
• Support video architecture decisions for multi-camera vehicle deployments and future intelligent video features

Required Qualifications

• 3+ years of experience building or maintaining real-time camera or video systems
• Strong understanding of frames, buffers, latency, throughput, and bandwidth in live video pipelines
• Experience with V4L2, GStreamer, or similar media pipeline frameworks
• Experience with hardware video encoding and decoding, especially H.264 and H.265
• Experience building or maintaining multi-stream video pipelines
• Ability to debug frame drops, synchronization issues, memory bottlenecks, and performance problems in production systems
• Strong systems-level programming skills in C, C++, Rust, or similar languages
• Comfort working close to hardware, drivers, and edge devices

Preferred Qualifications

• Experience with multi-camera systems in embedded or edge environments
• Familiarity with ISP tuning, camera sensor behavior, and image pipeline optimization
• Experience with edge AI or computer vision pipelines running alongside video workloads
• Background in fleet, automotive, robotics, surveillance, or other real-time video-heavy systems
• Experience optimizing pipelines under strict device, thermal, or bandwidth constraints