Perception Engineer

Who We Are:

Aurelius Systems is a VC backed defense tech startup building autonomous, edge deployed directed energy systems for counter‑UAS. We build laser weapons to shoot down drones.

We're a small team of engineers, former US military operators, and subject matter experts scaling America's directed energy dominance. The first cost effective, reliable and robust laser weapon system.

Our namesake isn't an accident. Marcus Aurelius wrote about doing the work in front of you, every day, without excuses. Henry Ford didn't wait for permission to reinvent manufacturing. That's how we operate — small team, unreasonable output, no hiding behind the unachievable.

In addition to our San Francisco lab, we’ve opened a Detroit manufacturing hub and we field test weekly on our own 400‑acre private range.

We are seeking a skilled Perception Engineer to join our software team and work on end‑to‑end perception and sensor‑fusion stack. You will develop, train, and deploy vision and sensor based models; manage data pipelines; and enable real‑time detection and tracking for our laser‑based defense system. This role bridges data science, software engineering, and robotics to deliver reliable, high‑throughput perception performance on edge hardware.

• Design, train, validate and fine‑tune machine‑learning and deep‑learning models (e.g., YOLO, RT‑DETR, CNNs) for object detection, classification, and segmentation.
• Integrate and fuse data from multi‑modal sensors (RGB, thermal, LiDAR/ToF, IMU, encoders) to produce robust, real‑time Regions of Interest (ROIs).
• Research, implement, and as‑needed develop high and low‑level image‑processing techniques, such as deconvolution, low SNR detection, and motion‑isolation techniques.
• Collaborate with hardware teams to integrate and troubleshoot sensors (global‑shutter and rolling‑shutter cameras, thermal imagers, LiDAR/ToF modules, IMUs) over GigE Vision, USB3 Vision, CAN, SPI, and I²C protocols; develop and debug embedded firmware in C/C++ (or Rust) for microcontrollers (STM
  32, NXP, TI) and FPGAs using VHDL/Verilog within RTOS environments (FreeRTOS, Zephyr).
• Build scalable data ingestion, labeling, augmentation, and storage pipelines (simulated and field data) ensuring 100k+ labeled frames accuracy.
• Optimize inference frameworks for edge deployment (GPU/FPGA), achieving ≥500 Hz end‑to‑end throughput.
• Develop dashboards and telemetry for drift analysis, hardware health monitoring, performance metrics, and automated retraining triggers.
• Author clear technical docs; mentor junior engineers on best practices in vision, sensor‑fusion, and embedded firmware engineering.
• Determine development needs by directly analyzing technical and physical limitations of our goals.

• 2–6+ years in computer vision, sensor fusion, or robotics perception roles
• Strong C++ with deep ML engineering experience
• Hands‑on with ML frameworks (Tensor Flow, PyTorch) and real‑time inference engines (TensorRT, OpenVINO)
• Computer vision, tracking, and detection in real‑time, real‑world conditions
• Familiarity with ROS2, Docker, and CI/CD for ML pipelines
• Experience with multi‑sensor calibration and data synchronization

Perception roles at defense companies, autonomous vehicle programs, robotics platforms, or aerospace programs that deploy perception against real targets.

You’ve shipped a perception system that ran on real hardware against real targets. You know the gap between paper SOTA and what survives field conditions.

Your background is research only without deployment, or your CV experience is offline batch processing only.

• Edge‑AI optimization (quantization, pruning)
• Experience with FPGA or embedded GPU platforms
• Background in defense or safety‑critical systems
• Familiarity with cybersecurity guidelines and secure coding practices

• BS, MS, or PhD in CS, EE, Robotics, or equivalent. Track record matters more than degree.

• Extreme bias for action. You ship working perception on real hardware, not slideware
• You debug from first principles, not intuition alone
• Comfortable with…