Applied Scientist Intern, Computer Vision (Part-Time, PhD)
SafeLab AI EngineeringRemoteposted 4d ago
- PayUSD / yr
- $166k – 250k
- Where
- Remote (US)
- Posted
- Oct 31
- 4 days ago
- Type
- Part-time
- Setup
- Remote
EMPLOYMENT
Part-time
COMPENSATION
$166k - 250k/yr
ARRANGEMENT
Remote
At a glance
Train and evaluate computer vision models for low-data client problems, combining deep learning with geometry and classical methods before delivering deployable ONNX or TensorRT prototypes.
Summarized by AI from the original posting
What you'll do
- Scope computer vision problems, tasks, metrics, and targets
- Train and fine-tune task-specific vision models
- Combine learned models with classical computer vision methods
- Plan data strategies for low-data settings
- Deliver auditable prototypes with error analysis
- Export models for deployment using ONNX and TensorRT
Requirements
- 01First-author paper at a peer-reviewed computer vision or machine learning venue
- 02Experience training and evaluating vision models in PyTorch
- 03Experience with collected or labeled datasets
- 04Working knowledge of OpenCV and projective geometry
- 05Prior experience in document image analysis, tracking, pose estimation, 3D reconstruction, or point clouds preferred
- 06Inference optimization experience preferred
- 07Prior work with industry data or external collaborators preferred
Perks
Remote workHybrid work optionFlexible part-time hours
Skills
PythonPyTorchYoloDetrMask R CnnU NetOpencvOnnxTensorrtOnnx RuntimeHough TransformsHomography Estimation
Full description
About SafeLab
SafeLab AI Engineering provides custom AI and ML solutions to enterprises worldwide.
What you’ll work on
• Scope incoming problems: define the task (detection, segmentation, keypoints, tracking, reconstruction), the metric (mAP, IoU, HOTA, Chamfer distance), and a realistic target before building
• Train and fine-tune task-specific models, such as YOLO or DETR-family detectors, Mask R-CNN or U-Net segmentation, and keypoint models, on client datasets that often contain only a few hundred images
• Combine learned models with classical methods where they work better: line and contour extraction, Hough transforms, homography estimation, camera calibration, multi-view geometry
• Plan data strategy for low-data settings: annotation protocols, augmentation, synthetic data, active learning
• Deliver working prototypes with error analysis a client engineer can audit, and export models for deployment (ONNX, TensorRT)
Required
• PhD candidate in Computer Science, Electrical Engineering, or a related field
• First-author paper at a peer-reviewed CV or ML venue (CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, WACV, BMVC, or similar); please list it on your CV
• Experience training and evaluating vision models in PyTorch on datasets you collected or labeled
• Working knowledge of OpenCV and projective geometry
Preferred
• Experience in document image analysis or vectorization, multi-object tracking, pose estimation, 3D reconstruction, or point clouds
• Inference optimization experience (quantization, pruning, ONNX Runtime, TensorRT)
• Prior work with industry data or collaborators outside your lab
Details
• Part-time with no minimum hours; work scales with client projects and fits around your research
• Remote, or hybrid from our Irvine, CA office
• Pay: $80 to $120 per hour