Note: The job is a remote job and is open to candidates in USA. Amniscient LLC is hiring a Machine Learning Engineer II to build computer vision systems that turn images into labeled datasets, train and benchmark models at scale, and deploy them to production. The role focuses on object detection, data pipelines, model optimization, and production monitoring, in collaboration with data, platform, and product teams.
Responsibilities
- Train, fine-tune, and evaluate object detection models (e.g., YOLO-family, DETR-family, Faster R-CNN) for production use cases
- Build and maintain data pipelines for image and video datasets, including labeling workflows and quality checks
- Define evaluation metrics (mAP, per-class precision/recall, IoU, latency) and run error analysis to decide what to fix next
- Optimize models for deployment through quantization, pruning, and export to ONNX or TensorRT
- Deploy and monitor models in production, tracking accuracy drift and data distribution shift
- Run experiments with clear tracking and reproducible results
- Write clean, tested Python code and take part in code reviews
- Share findings with product and engineering stakeholders in plain terms
Skills
- 2–5 years of professional experience building ML systems, with 2+ years focused on computer vision
- At least one object detection model shipped to production and maintained after launch
- Strong Python skills and hands-on experience with PyTorch
- Working knowledge of modern detection architectures, including one-stage, two-stage, and transformer-based detectors
- Solid grasp of detection fundamentals: anchor-based and anchor-free methods, IoU, Non-Maximum Suppression, mAP, and class imbalance
- Experience with image augmentation, dataset curation, and annotation quality control
- Comfort with Git, Linux, Docker, and training on GPUs
- BS in Computer Science, Computer or Electrical Engineering, or a related field
- MS or PhD with a computer vision focus
- Experience with multi-object tracking, instance segmentation, or video-based detection
- MLOps experience: experiment tracking, model registries, CI/CD for models, and production monitoring
- Distributed training across multiple GPUs
- Edge deployment (NVIDIA Jetson, mobile, or embedded) and inference optimization with ONNX Runtime or TensorRT
- Publications, open-source contributions, or competition results in computer vision
Benefits
- Remote or hybrid work arrangement in Houston, TX.
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