Founding Systems Engineer

AI Talent Now; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

AI Talent Now is assembling a hardware-software team in San Francisco to build wearable AI copilots for industrial technicians. The role centers on moving sensor data from camera hardware to on‑device models with ultra-low latency, including edge inference when networks lag.

You will own end-to-end systems—from camera input to model output—working closely with the hardware team. Prior startup exposure and hands-on device experience are highly valued.

Qualifications

• Hands-on device software development for real-time sensor data.
• Experience end-to-end hardware/software integration.
• Familiarity with edge inference and low-latency networks.

Responsibilities

• Own the path from camera to the model and back.
• Develop on-device and edge code with low-latency transport.
• Ship software moving sensor data from device to model in real time.
• Collaborate with hardware and firmware teams for wearables.

Skills

C/C++ on device

Rust on device

RTOS / embedded Linux

End-to-end ownership

Python + AWS/GCP

Education

Top CS/Eng program

Tools

WebRTC

GStreamer

TensorRT

Job description

San Francisco, United States | Posted on 11/11/2026

AI Talent Now, LLC is a powerhouse in direct-hire talent acquisition, connecting exceptional talent with industry-leading organizations across IT, Engineering, Financial Services & Fintech, and Manufacturing & Robotics. Headquartered in Atlanta, Georgia, we proudly serve clients and candidates nationwide.

Job Description

AI Talent Now Job ZR 112 AL

Why This Company?

Real Impact:Agentic VLM smart glasses already deployed at world-leading data center and energy grid customers. Two of their accounts alone are on path to $10M ARR, with 10-15 more enterprise prospects in pipeline (hyperscalers, top aerospace primes, US Army).

Differentiated Bet:Computer vision and agentic VLM grounded on bespoke integrations with legacy enterprise systems (Salesforce, ServiceNow, SAP). Not another LLM wrapper.

Strong Seed & Investors:$5M led by A* with deep operator network, and Valor as strategic with direct XAI / data center introductions. Founder-led sales already converting.

Exceptional Early Team:You'll get to work with exceptional early hires: Stanford PhD dropout and olympiad medalist & ex-YC engineer, alongside the French co-founder duo (mechanical engineering + AI computer vision research). Industrial-minded from day one.

Why this Role?

Closest to the hardware:you own the path from the camera to the model and back.

What you build:device bring-up, on-device and edge code, low-latency transport over bad industrial networks, and the specs we hand to manufacturers.

Real stakes:real hardware at real customer sites, founding equity, direct work with the founders.

This company is building wearable AI co-pilots for industrial field workers, deploying smart glasses powered by agentic vision-language models (VLMs) to legacy industries like data centers, energy grids, and aerospace. The company is on a mission to bring AI into environments where technology—let alone artificial intelligence—has never meaningfully helped before.

Founded by two French co-founders who have been best friends since age three, This company combines deep expertise in mechanical engineering, AI, and robotics with a long-term vision for industrial automation. The company operates at the intersection of computer vision, multimodal AI, and wearable hardware, collecting real-world data that can serve robotics automation down the line. The founders are joined by exceptional early hires: Stanford PhD dropout and olympiad medalist & ex-YC engineer.

Culture:

This company runs with the intensity of a top-tier YC-style startup. The team values hunger, scrappiness, and genuine mission alignment over pedigree. Engineers are expected to operate with high ownership, move fast, and iterate constantly as the AI landscape evolves. The founders prioritize people who are excited about computer vision and industrial applications—not just chasing the latest LLM trends. The work environment is collaborative, in-person, and high-tempo, with a strong emphasis on shipping production systems to real customers.

With anchor accounts at world-leading data center and energy grid companies already on path to $10M ARR and 10-15 enterprise prospects in pipeline (including hyperscalers, top aerospace primes, and US Army), this company is solving one of the most interesting applied AI problems in the market today.

This company builds a real-time AI co-pilot that runs on smart glasses and guides industrial technicians through hands-on maintenance and repair in data centers, energy and aerospace. A vision-language model sees what the technician sees and talks them through the job, live.

Backing and traction:

$5M seed led by A*, with Valor as strategic investor. Two anchor customers (world-leading data center and energy grid companies) are already on a path to $10M ARR, with 10–15 more enterprise prospects in the pipeline, including hyperscalers, top aerospace primes and the US Army.

The role:

you own the path from the camera to the model and back. Video, audio and sensor data leave a device on someone's head, cross a bad factory-floor network, reach our models, and the answer comes back fast enough to feel live. You bring up the camera hardware (smart glasses and other wearable cameras), write the on-device and edge code and the low-latency transport, run inference at the edge when the network can't keep up, and write the device specs we hand to hardware manufacturers. You are the engineer closest to the hardware on a small founding team.

You've done this before.

In a full-time role, you shipped software that moves sensor data off a physical device in real time, and you can say what you built yourself. For example:

• The onboard perception or sensor stack on a robot, drone or vehicle
• Sensor bring-up and low-latency firmware on a humanoid or a robot arm
• The teleoperation video link for an autonomous fleet
• Edge inference on camera streams (Jetson, Snapdragon, TensorRT) under latency and power limits
• Real‑time video or voice transport from a device (WebRTC, RTSP / RTP, GStreamer)
• Camera, IMU or audio streaming from a wearable or AR headset

What they're looking for:

• 1–6 years of full‑time engineering with hands‑on device work. Founders and ex‑founders are very welcome.
• C / C++ or Rust on the device side; RTOS or embedded Linux; you profile against memory, power and latency budgets.
• You owned a system end to end: as a founder, as a founding or early engineer at a hardware or robotics startup, or as the DRI for a shipped subsystem.
• Bonus: smart glasses or AR camera pipelines, AV teleop, drones, evaluating camera modules and writing ODM specs, edge‑to‑cloud fleet telemetry, enough Python and AWS / GCP to stand up the ingest service.

Work Experience

Full‑time, hands‑on work on a physical device that produces sensor data (camera, video, audio, LiDAR, IMU, encoders): robots, drones, vehicles, wearables, cameras. Device work only in internships, research or student teams does not count.

Owned real‑time work on that data: onboard runtime or perception, sensor bring‑up or firmware, edge inference under latency / power limits, or device‑to‑cloud video / sensor streaming (WebRTC, RTSP, GStreamer).

Education

Top CS/Eng program or top‑tier company/startup, or a standout signal (olympiad, competitive programming, founded while studying)

Updated

Deep in one of: low‑latency streaming over unreliable networks (WebRTC/RTP/UDP), on‑device camera/sensor capture or firmware, or edge inference on camera / sensor input under latency and power limits (not cloud GPU / LLM serving); credible in the others.

Baseline

Seniority

1 - 6 years of experience post‑grad full‑time engineering with hands‑on device work; founders and ex‑founders welcome

Ownership

founder, founding or early engineer at a hardware / robotics / device startup, or DRI for a shipped device subsystem.

Updated

Hands‑on in C / C++ or Rust on the device side; RTOS or embedded Linux; profiles against memory / power / latency budgets.

Ships fast with AI coding tools, and can explain every design choice without them

Updated

Ideally willing to join a hacker house (live on site). At least willing to work on‑site 5 days/week in SF.

Nice‑to‑have

Smart glasses / AR camera pipelines, AV teleop, drones, or writing ODM / camera specs.

Education

Grit proxies: olympiad medals, USACO Platinum, competitive programming rank, or founded‑while‑studying.

Updated

Enough Python and AWS / GCP to stand up the ingest service.