2

Entry Level Wireless Sensor Network Jobs (NOW HIRING)

Software Engineer - Systems

San Francisco, CA · On-site

$203K - $241K/yr

We offer both long range wireless (1km range) and wired sensor variants to suit any deployment. Our ... Design and build low-latency networking infrastructure connecting embedded devices and cloud ...

This role supports the engineering, integration, and optimization of secure wired and wireless ... Thorough understanding and substantial experience in radio frequencies, sensor integration, and ...

Network Administrator I

Boston, MA · On-site

$38.99 - $50.68/hr

Respond to service requests and incidents related to network connectivity, wireless access, and VPN ... entry-level IT roles. Candidates should demonstrate a foundational understanding of networking ...

Basic wired and wireless networking concepts * Entry-level troubleshooting skills with the ability to analyze and resolve network issues Soft Skills: * Excellent problem-solving skills * Strong ...

Be Seen First

In this role, you'll help customers explore wireless technology, smartphones, and cellular service ... Team-building events and networking opportunities * A supportive, high-energy culture that ...

Be Seen First

In this role, you'll help customers explore wireless technology, smartphones, and cellular service ... Team-building events and networking opportunities * A supportive, high-energy culture that ...

next page

Showing results 1-20

Entry Level Wireless Sensor Network information

See salary details

$10

$52

$82

How much do entry level wireless sensor network jobs pay per hour?

As of Jul 29, 2026, the average hourly pay for entry level wireless sensor network in the United States is $52.57, according to ZipRecruiter salary data. Most workers in this role earn between $40.38 and $62.02 per hour, depending on experience, location, and employer.
What are the most commonly searched types of Wireless Sensor Network jobs? The most popular types of Wireless Sensor Network jobs are:

Software Engineer - Systems

Specter

San Francisco, CA • On-site

$203K - $241K/yr

Full-time

Re-posted 29 days ago


Job description

Company BackgroundSpecter'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 RoleSpecter 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