---
title: 'Software Engineer - Systems at Specter'
canonical: 'https://feeny.ai/job/software-engineer-systems-specter-san-francisco-5j7d7zwdxhsr'
type: 'job'
last_seen: '2026-09-07'
---

# Software Engineer - Systems at Specter

- **Company:** Specter
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2025-10-03
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/specter/fe26a690-7934-46f4-bce2-8d8c5495a0dc

## 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

## About Specter

## Company Overview
- **One-liner**: Specter builds and deploys long-range wireless sensing networks with AI-powered real-time alerts and semantic search, creating the perception layer for the physical world.
- **Entity Type**: Private (funding stage not publicly disclosed)
- **Headquarters**: Not publicly available (likely US based on website language and job postings)
- **Founded**: Not publicly available
- **Founders**: Not publicly available

## Core Business
- Primary industries: Physical security, critical infrastructure monitoring, industrial IoT, real-time situational awareness
- Target customers: Enterprise and government – including energy companies, data centers, construction firms, ports, stadiums, industrial facilities, and campuses
- Mission statement: "Creating a Software Defined Physical World" – providing instant awareness through real-time alerts, semantic search, and a map-based view of every event

## Products & Services
- **Specter Platform**: A SaaS-based physical intelligence platform that ingests data from long-range wireless sensors (video, thermal, acoustic) and applies general AI (natural language alerts) to detect events such as unauthorized access, safety hazards, equipment anomalies, fires, spills, and perimeter breaches. Features include semantic search, live and historical event detection, and complete situational awareness dashboards.
- **Long-Range Wireless Sensing Network**: Proprietary hardware and software for rugged, wide-area coverage without trenching – deployed across refineries, deserts, offshore platforms, construction sites, and other harsh environments.

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Annual revenue not publicly available; company is actively hiring (Software Engineer – Systems listing) and has a live product across multiple industry verticals.
- **Notable Investors/Partners**: Not publicly available
- **Growth Signals**: Recent website updates as of June 2026; expanding to new use cases (energy, data centers, construction, ports, stadiums); job openings indicate scaling engineering team.

## Competitive Advantages
- **Long-range wireless sensing** that eliminates blind spots without trenching or on-site personnel – a differentiator for remote and harsh environments.
- **General AI with natural language alerts** – users set and query alerts in plain English, lowering the barrier for security and operations teams.
- **Multimodal sensing** (video, thermal, acoustic) combined with real-time analytics for high-fidelity event detection (e.g., PPE compliance, spills, equipment theft, person-down incidents).
- Focus on **critical infrastructure** where downtime or security gaps carry high stakes, creating deep vertical-specific moats.

## Strategic Focus
- Expanding horizontal deployment across energy, data centers, construction, logistics, and campus security.
- Deepening AI capabilities through real-time semantic search and event detection to replace traditional CCTV monitoring.
- Building a “software-defined physical world” that unifies disparate sensors into a single intelligent layer.

## Why Work Here
- Opportunity to work on cutting-edge AI + IoT systems that monitor real-world infrastructure at scale.
- Engineering culture likely emphasizes distributed systems, computer vision, edge computing, and high-reliability software (based on job description for Software Engineer – Systems).
- Fast-paced startup environment with a clear product-market fit across multiple verticals.
- Remote/hybrid policy not explicitly stated; job posting does not specify location, but likely offers flexibility.

## Sources
1. [specter.co](https://specter.co/)
2. [specter.co/product](https://specter.co/product)
3. [specter.co/industries](https://specter.co/industries)
4. [specter.co/features](https://specter.co/features)
5. [jobs.ashbyhq.com - Software Engineer – Systems](https://jobs.ashbyhq.com/specter/fe26a690-7934-46f4-bce2-8d8c5495a0dc)

## Other roles at Specter

- [Wireless Software Engineer](https://feeny.ai/job/wireless-software-engineer-specter-san-francisco-zvjchm89fzv0) — San Francisco, CA
- [Software Engineer - Applied AI](https://feeny.ai/job/software-engineer-applied-ai-specter-san-francisco-p7er9310dx06) — San Francisco, CA
- [Senior Antenna Engineer](https://feeny.ai/job/senior-antenna-engineer-specter-san-francisco-xea2760wvrkw) — San Francisco, CA
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