Software Engineer - Systems

Job description

**Company Background**Specter's mission is to help automate the physical world. Today, we build video sensors with state-of-the-art AI agents that answer any question, anywhere in their environments. Our systems can automatically detect and reason about any physical activity captured on camera, from security incidents (e.g. perimeter intrusion, theft, LPR), to safety monitoring (e.g. PPE detection, injured people), to operational efficiency (e.g. material tracking, congestion monitoring). We offer both long range wireless (1km range) and wired sensor variants to suit any deployment. Our co-founders Xerxes and Philip are passionate about empowering our partners in the fast approaching world of physical AI and robotics. We are a small, fast growing team who hail from Anduril, Tesla, Uber, and the U.S. Special Forces. **The Role**Specter is hiring a Software Systems Engineer to build the real-time device software at the heart of our platform — spanning sensor integration, video pipelines, low-latency networking, and the infrastructure that ties it all together. This role owns the full stack from hardware interface to cloud edge, working closely with ML, perception, and platform teams to ship the performant, reliable systems that power autonomous monitoring across our customers' physical environments. **Responsibilities:** - Design and build low-latency networking infrastructure connecting embedded devices and cloud systems — protocol design, congestion handling, and tuning for throughput and reliability across a distributed sensor network - Build resource-efficient pipelines to ingest and egress multimodal sensor data and telemetry, handling packetization, buffering, and backpressure across constrained device environments - Own low-latency command and control infrastructure across a distributed sensor network, with a focus on fault tolerance, deterministic timing, and graceful degradation - Integrate and fuse multimodal data streams from cameras, IMUs, and other sensors — working across driver boundaries, synchronization, and calibration to produce reliable inputs for downstream algorithms - Build and optimize video and image processing pipelines end-to-end: capture, hardware-accelerated encode/decode, streaming, and storage - Contribute to tracking and state estimation algorithms, bridging raw sensor data and meaningful system outputs in close collaboration with ML and perception teams - Build and maintain CI pipelines, test harnesses, and reliability tooling — the simulators and replay systems that let the team move fast without breaking things in the field - Instrument, profile and benchmark system performance — CPU/GPU utilization, memory pressure, network throughput and latency — and drive systematic improvements **Qualifications:** - Broad systems experience across the areas below, with demonstrable depth in at least one — whether that's networking, video/sensor pipelines, or low-level Linux systems work - Production Rust (preferred) or C++ in low-latency, embedded, or systems contexts — with real ownership of performance, reliability, and resource constraints - Deep networking knowledge (UDP, TCP, QUIC) beyond the API level — packet loss, flow control, retransmission, and tuning for real-world conditions; strong Linux systems fundamentals including IPC, scheduling, and memory management - Hands-on hardware integration experience — cameras, IMUs, or other sensors — including driver interfaces, kernel boundaries, and video pipelines (capture, encode/decode, streaming via V4L2, GStreamer, FFmpeg, or similar) - Proficiency with concurrency and parallel programming — lock-free structures, async runtimes, thread management — with a track record of shipping correct, performant, concurrent code - Comfortable owning CI infrastructure, test harnesses, benchmarking pipelines, and observability tooling alongside feature work **Nice to Have** - Experience working alongside real-time, multimodal ML data ingestion systems — understanding the data quality, latency, and throughput requirements that make or break model performance - Hands-on experience with modern video codec implementations (H.264, H.265, AV1) across hardware platforms — encoder tuning, rate control, and platform-specific acceleration (V4L2, NVENC, etc.) - Robotics, perception, or state estimation background — familiarity with sensor fusion, localization, tracking algorithms - Experience writing Rust and Nix