# Principal Computer Vision Engineer

**Company:** [Instacart](https://hotfix.jobs/companies/instacart)
**Location:** Unspecified
**Role:** AI Research
**Experience:** 7+ years
**Skills:** Computer Vision, Machine Learning, Optical Flow, Multi-View Geometry, Feature Matching, Homography Estimation, Opencv, Deep Learning, Object Detection, Image Classification, Semantic Segmentation, Instance Segmentation, Python, C++, PyTorch
**Posted:** 2026-08-24

> Leads the architecture and production deployment of real-time computer vision and perception systems, combining classical techniques with deep learning. Requires principal-level technical leadership, 6+ years of industry experience, Python, PyTorch, and real-time optimization expertise.

## Job Description

## Responsibilities
- Architect and own computer vision pipelines from research and training through optimized production deployment.
- Design classic computer vision algorithms and deep learning architectures for spatial, alignment, and tracking challenges.
- Train, evaluate, and fine-tune models for object detection, image classification, and semantic or instance segmentation.
- Build and optimize low-latency inference pipelines in Python and C++ using TensorRT and CUDA.
- Drive technical strategy, evaluate architectures, standardize training methodologies, and balance accuracy, speed, and compute constraints.
- Deliver technical design, implementation, observability, and iterative improvements for the perception engine.

## Requirements
- 6+ years of industry experience in computer vision, machine learning, or a related field.
- Deep expertise in optical flow, multi-view geometry, feature matching, and homography estimation, including OpenCV.
- Strong understanding of deep learning for object detection, image classification, and semantic or instance segmentation.
- Production-level Python programming skills.
- Extensive experience with PyTorch and scalable model training and evaluation pipelines.
- Experience optimizing algorithms for resource-constrained or real-time deployment.

## Nice-to-haves
- Experience with segmentation frameworks and zero-shot or foundation models, including SAM, Mask2Former, and Grounding DINO.
- Experience integrating computer vision models into native iOS or Android SDKs.
- Experience translating computer vision algorithms into Apple frameworks and iOS.
- Retail technology or AR-based scanning experience.

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