Join Specter as a Software Systems Engineer to build real-time device software for autonomous monitoring systems.
Posted by employer 1 year ago
First seen on Joblaze 53 minutes ago
Last verified on the company career page 53 minutes ago
What you'll build
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Nice to have
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Joblaze summary
The Software Engineer - Systems at Specter focuses on developing real-time software that integrates various sensors and manages low-latency networking for their AI-driven video systems. Key skills include proficiency in Rust or C++, deep networking knowledge, and experience with hardware integration and video processing pipelines. This role is ideal for someone with a strong systems background, particularly in embedded or low-latency contexts, who can navigate complex technical challenges. Specter's small, fast-growing team emphasizes collaboration across disciplines, including machine learning and perception.
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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
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