This job board retrieves part of its jobs from: Toronto Jobs | Hamilton Jobs | Work From Home

Daily updated job offers in California

To post a job, login or create an account |  Post a Job

  OurOnlinejobs.com  

Helping you to find a new job in California

previous arrow
next arrow
Slider

Computer vision and machine learning internship opportunities

Stealth

This is a Full-time position in San Francisco, CA posted December 14, 2019.

AboutWe are a stealth startup in the autonomous driving space based in the Bay Area and DC metropolitan region, funded by a well-known Tier 1 venture firm.

Our core team includes faculty entrepreneurs (Stanford and UC Santa Barbara) and veterans from industry (Uber, Apple, Samsung, Amazon Lab126), who have successfully shepherded signal processing and machine learning innovations to large-scale software for location improvement and safety at Uber, led the development of state-of-the-art computer vision technologies that shipped over millions of Amazon devices, and have delivered zero-to-one product experiences at Uber and Box.Our mission and team expertise spans beyond software to advanced sensor systems, embedded systems, signal processing, computer vision and machine learning.

We look for people with a depth of expertise and experience in one of these areas, and with the intellectual curiosity for interacting with, learning from, and teaching world-class experts in areas outside their expertise.We currently have multiple internship opportunities in the areas of computer vision and machine learning.

The candidate will join a multi-disciplinary team of scientists and engineers and work on full stack of developing cutting edge Computer Vision(CV) and Machine Learning(ML) methods based on data from a variety of different sensors. ResponsibilitiesDevelop state of the art deep learning networks and architectures across data from multiple sensors; Tasks include training, evaluating, benchmarking and deployment into real-time pipelinesPerform run-time network optimizations such model quantizations, pruning, and knowledge distillation.Development of domain adaptation networks and simulation techniques to generate custom sensor-specific data for augmentationDevelopment of high efficiency machine learning/deep learning models for problems with access to limited quantities of annotated dataBasic QualificationsOn-going studies toward an MS or PHD Strong Python programming, software development best practices, debugging/profilingExtensive experience with at least one main stream deep learning framework such as PyTorch or TensorFlow Experience with some tensor processing libraries with Python (python tensor, numpy, scipy, pandas, sci-kit-learn, sci-kit-image, etc.)Preferred QualificationsBackground with neural network architecture patterns for computer vision (classification/segmentation/detection), natural language processing or speech recognition (CNNs, RNN, LSTMs, Mask-RCNN,..

etc.) Background in semi-supervised, generative modelling and data synthesis networks (GANs, MUNIT, ..etc) Publications in major CV/ML conferences and journalsFamiliarity with traditional computer vision methods and signal processingStatistical modeling, analysis, and significance testingFamiliarity with data science toolkit such as jupyter lab/notebooks, pandas, bash scripting, Linux environment Experience with real-time modern programming languages: (Java, C/C++, JavaScript for Node).