# Software Engineer - Systems at Specter

- Company: Specter
- What the company does: Search 55M companies, 550M people and every funding or M&A event in real time—surface hidden startups, size markets and act first. Backed by Entrepreneur First.
- Company website: https://tryspecter.com
- Type: Startups
- Level: Mid level
- Location: San Francisco
- Work setup: On-site
- Posted: 2025-10-03
- Apply by: 2026-10-08
- Apply: https://jobs.ashbyhq.com/specter/fe26a690-7934-46f4-bce2-8d8c5495a0dc
- Page: https://www.1752.vc/careers/jobs/specter-software-engineer-systems/

## About the role

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

## What they're looking for

- 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

Tags: Engineering