About Safari AI
's (fka curbFlow) computer vision AI technology enable its enterprise clients to automatically measure the most important physical activity in their operations using the clients' own IP cameras. For example, Safari AI provides live wait times and throughput for Legoland, measures drive-thru donations at Goodwill, and provides pedestrian counts to Taco Bell / Yum Brands.
Safari AI is venture funded by General Catalyst and Initialized Capital and has raised $13mm to date.
Leadership & Culture
Safari AI is founded by a former Y Combinator founder with a meaningful exit under his belt. The company has headquarters in NYC, and a distributed engineering team with members living throughout North and South America.
We are seeking computer vision interns to join our team for 6-12 months, with the opportunity to become a full-time team member afterwards pending performance. Interns will work closely with our engineering team on deploying new locations and features for our clients. Interns will have an opportunity to work with cutting-edge AI technology and to be part of a dynamic and innovative team.
Implement computer vision solutions in the areas of object detection and tracking, scene segmentation, scene understanding, and depth estimation
Build solutions that are robust to camera motion, occlusions, poorly exposed scenes
Work with a growing team of infrastructure and machine learning engineers of various levels
Define labeling ontologies and create training, validation, and testing sets across customer sites and weather conditions
Onboard customers with machine learning solutions and calibrate models for satisfactory accuracy
Senior undergraduate or graduate school students in computer science or relevant field with exposure to classic and modern computer vision and machine learning techniques
Excellent written and verbal communication skills
Self-motivated, critical thinking and enthusiastic in solving real-world problems
Python experience/expertise training, evaluating, and deploying models with one of more common deep learning framework such as PyTorch or Tensorflow
Sufficient familiarity with traditional computer vision and machine learning techniques with good mathematics foundations and geometry knowledge
Deep insight and experience on modern computer vision and machine learning techniques (e.g., object detection, multi-object tracking)
Familiar with popular computer vision solution related frameworks, such as opencv, gstreamer, ffmpeg, deepstream, tensorrt, etc..
Experience with Linux environment and targeting embedded deployment
Experience with public cloud such as GCP, Azure or AWS
Ability to design, implement, present, and operate independently without oversight
Good business insight and exceptional analytical skills
Nice to have:
Experiences with data streaming frameworks, such as flink, spark, beam, etc.
Experiences in startup environment
Experiences with embedded platforms such as NVIDIA Jetson, Intel Movidus, etc.
Fluent in Java or Typescript
Publication records on top ML/CV conferences / journals such as CVPR, NeurIPS and ICCV