Sensys Networks is expanding its intelligent transportation platform into real-time video-based detection. We are looking for a hands-on engineer to contribute to the full detection pipeline running on embedded camera hardware --- from raw image input through object detection, tracking, and low-latency output to downstream traffic systems.

We're building a detection system from the ground up on a modern edge AI platform. You'll be a core contributor on a small team, working on model training, optimization for embedded hardware, and real-time inference. This is an opportunity to ship production detection code and grow your edge AI expertise on a system that matters.

The right person for this role is a computer vision and ML engineer first, software engineer second. You have trained detection models on real data, deployed them to edge hardware, and developed the intuition for what fails and why. You are comfortable with Python and C tooling, but the part of the job we value most is model tuning and optimization - making models small, fast, and accurate for deployment on resource-constrained edge devices.

What You'll Own

Train, fine-tune, and iterate on detection models for our specific domain --- work with datasets, augmentation, and transfer learning to get models working well on real data
Optimize trained models for deployment on edge hardware --- quantization, pruning, and tuning to hit latency and accuracy targets on the target device
Design and maintain the real-time video processing pipeline from raw image input through detection, tracking, and low-latency output
Develop and tune object tracking to maintain consistent identity across frames and detection zones
Work with the team on image pre-processing decisions (exposure, white balance, stabilization) as they affect model robustness; deep ISP tuning is handled separately
Work closely with the Systems Engineer to integrate detection outputs into our communication infrastructure
Contribute to camera selection and chipset evaluation for minimum viable processing requirements

What We're Looking For

2+ years of hands-on computer vision or video analytics, with at least one shipped detection model on edge hardware --- you've trained, evaluated, and iterated a model end-to-end
Experience deploying models on edge-AI hardware (TensorRT/Jetson, Hailo, OpenVINO, Coral, Ambarella, etc.) --- the workflow and constraints transfer across vendors
Hands-on experience training, fine-tuning, and retraining deep learning models for detection tasks --- you are comfortable working with datasets, augmentation strategies, and transfer learning to adapt existing models to new domains or data distributions
Proficiency with deep learning frameworks (PyTorch, TensorFlow, ONNX) and inference optimization toolchains (quantization, pruning, TensorRT, or vendor-specific compilers)
Solid understanding of object detection architectures (YOLO-family, SSD, Transformers) and multi-object tracking algorithms (SORT, DeepSORT, ByteTrack)
C comfort on embedded Linux --- you can read C, have cross-compiled, and debugged on a target board
Strong Python for model development and tooling
Comfortable working in an ambiguous, early-stage product environment, communicating trade-offs clearly and asking for direction when scope gets fuzzy

Bonus Points

Prior experience with traffic, automotive, or safety-critical vision systems
Hands-on experience with Ambarella chipsets and their toolchains
Experience with multi-sensor vision systems, camera synchronization, or coordinating detection across overlapping zones
Experience building scene-quality models that assess visibility degradation (weather, occlusion, lens obstruction)
Exposure to ISP tuning, camera firmware customization, or hardware-aware neural architecture search
Knowledge of H.264/H.265 encoding pipelines and their latency implications

The Opportunity

This role offers the chance to ship production detection code on a modern platform. You'll be a core contributor on a small team where your work directly impacts how the system performs. You're not salvaging legacy constraints --- you have real technical freedom on model selection, training strategy, and optimization decisions. The 6-month MVP target is aggressive and meaningful. Longer-term, the scope expands into intersection analytics, safety metrics, and predictive modeling.

We offer competitive compensation, a collaborative environment, and the satisfaction of shipping technology that makes real-world intersections safer and smarter.

Salary range: $130-$190K

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