Perception Engineer

Aurelius Systems•San Francisco, CA

17h•Onsite

About The Position

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 are a small team of engineers, former US military operators, and subject matter experts scaling America's directed energy dominance. 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.

Requirements

2–6+ years in computer vision, sensor fusion, or robotics perception roles
Strong C++ with deep ML engineering experience
Hands-on with ML frameworks (TensorFlow, 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
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 ambiguity and fast iteration in a startup environment
Clear communicator across software, hardware, and operator-facing surfaces
Self directed. You identify what needs to happen next and do it without being told
This role requires access to export-controlled information or items that require "U.S. Person" status.

Nice To Haves

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

Responsibilities

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 (STM32, 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.
Determines development needs by directly analyzing technical and physical limitations of our goals.