About Team
We build cameras for AI perception, not for human eyes. This internship contributes to the perception pipeline - data, models, deployment, and test - for low-power camera/sensing systems on wearable devices, working alongside a engineer/researcher.
We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies.
Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.
Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).
Responsibilities
- Contribute to power- and compute-efficient machine vision perception pipelines: data preparation, model training, quantization/optimization, and edge deployment.
- Help build and run test platforms and benchmarks for camera/perception performance (latency, power, accuracy).
- Support camera/sensor experiments: ISP-to-perception data flows, capture, calibration, and evaluation.
- Prototype and iterate on scoped components with guidance.Minimum Qualifications
- Currently enrolled in a PhD (or exceptional undergraduate) in EE, ECE, CS, Physics, or a related technical discipline.
- Solid programming in Python and/or C/C++; PyTorch or equivalent.
- Hands-on experience in at least one of: computer vision / ML, embedded/edge systems, or camera/image-sensor/imaging pipelines - with genuine exposure to cameras, images, or visual perception (adjacent-only modalities such as MRI/ultrasound/biosignals do not count).
- Able to execute a scoped project with guidance in a fast-paced, multidisciplinary team; good communication.
Preferred Qualifications
- Efficient/edge model deployment: quantization, pruning, ONNX/TensorRT, NPU/DSP.
- Image/video CV, 3D vision, or event/depth sensing experience.
- Embedded/hardware exposure: sensor bring-up, FPGA, PCB, or camera-pipeline work.
- Comfort leveraging modern ML tools (VLM/LLM) in the workflow.