Perception Lead - Drone Vision & Autonomy

Neros Technologies; Torrance (CA); On-site; USD 134,000 - 187,000; Full time

Job summary

Neros Technologies is seeking a Perception Lead to own the perception stack across unmanned aircraft, guiding a small team and shaping the transition from classical CV to learned models. You will own detection, tracking, and data-driven evaluation while ensuring real-time performance on constrained onboard hardware.

Ideal candidates have extensive experience with CV, C++, Python, and deploying neural networks to embedded accelerators, plus deep knowledge of EO/IR imaging and long-range detection

Qualifications

• 6+ years developing production computer vision or perception software, including 2+ years leading a team or a technical program as a tech lead or engineering manager, with continued hands-on technical contribution
• Delivery of perception software onto a physical system operating in an uncontrolled outdoor environment, such as unmanned aircraft, ground robotics, autonomous vehicles, or aerospace platforms
• Expert proficiency in C++ and Python, including production experience under real-time or resource-constrained requirements
• Depth in both classical and learned perception, spanning correlation, template and optical-flow tracking, feature matching and data association, alongside modern detection and classification networks
• Experience deploying neural networks to embedded accelerators such as NVIDIA Jetson and TensorRT, Qualcomm, Hailo, or equivalent, including quantization and characterization of the resulting accuracy and latency trade-off
• Working knowledge of EO and IR/thermal imaging, camera calibration, and sensor characteristics, with experience in small-object detection at long range, in low light, or under high dynamic range
• Experience defining perception evaluation, including dataset and labeling requirements, detection and tracking metrics such as precision, recall, track continuity and identity switches, and regression testing against logged flight or field data
• Experience selecting detection thresholds and operating points against an explicit cost of false positives versus missed detections in a safety-critical or mission-critical system
• Familiarity with sensor timing and synchronization, coordinate frames, and uncertainty representation at the interface to state estimation and navigation

Responsibilities

• Own the architecture, roadmap, and delivery of the perception stack across all vehicle platforms, maintaining a single configurable implementation rather than per-program forks
• Lead and grow the perception engineering team through hiring, mentorship, and technical review, while continuing to contribute directly as an engineer
• Deliver detection, recognition, and image-space tracking that hold a designated target and re-acquire it after loss, in clutter, glare, low light, occlusion, and degraded link conditions
• Define the perception output contract, including calibrated confidence and explicit signaling of suppressed or degraded detection, and set detection operating points jointly with the state estimation and engagement authorization owners
• Drive the progression from classical computer vision to learned detection and classification, determining when flight data demonstrates that a learned component outperforms the deployed baseline, defining the evaluation criteria and regression cases that decision rests on, and retaining a deterministic fallback
• Own deployment of perception software to constrained onboard compute, including accuracy and latency trade-offs, optimization for embedded accelerators, and compliance with end-to-end latency budgets

Skills

Computer vision

Unmanned platforms

C++

Python

Embedded accelerators

TensorRT

Sensor fusion

EO/IR imaging

Data evaluation

Job description

Neros Technologies is seeking a Perception Lead to own the perception stack across unmanned aircraft, guiding a small team and shaping the transition from classical CV to learned models. You will own detection, tracking, and data-driven evaluation while ensuring real-time performance on constrained onboard hardware.

Ideal candidates have extensive experience with CV, C++, Python, and deploying neural networks to embedded accelerators, plus deep knowledge of EO/IR imaging and long-range detection