Machine Learning Consultant

Company: Genius Business Solutions
Location: Not Specified, Not Specified, United States
Type: Full-time
Posted: 02.SEP.2021
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Summary

We are looking for a talented Machine learning expert with experience with front-end programming frameworks (such as React) and Javascript, ...

Description

We are looking for a talented Machine learning expert with experience with front-end programming frameworks (such as React) and Javascript, experience in Cloud & backend technologies, node.js, mongoDB, RPC, Restful etc & Golang, C++, Python, Java.

The candidate will leverage their expertise in machine & deep learning technologies to build a production quality MLModelScope system that can be deployed in hybrid cloud environments (public clouds and on prem systems) for large-scale adoption. Detailed documentation & information on the MLModelScope system will be made available once selected.

Job Description:

  • Enhance the software architecture to build various deep learning framework agents such that MLModelScope can support a more complete list of deep learning frameworks (such as Chainer, DeepLearning4J) with different versions. For example, the existing MLModelScope agents do not support the wide range of Tensorflow versions.
  • Extend MLModelScope agents to other deep learning and machine learning packages that may or may not build on top of other deep learning frameworks, such as Keras, DGL on PyTorch for graph neural network, Sonnet on Tensorflow, and Gluon on MXNet etc.
  • Enhance MLModelScope's across-stack profiling capabilities from model inference to model training. The current support of inference capabilities can be provided to the consultant.
  • Build the MLModelScope to work across a wide range of heterogeneous worker node systems (X86, AMD, ARM, POWER, FPGAs, and mobile devices)
  • Enhance MLModelScope to support its agents running on various Mobile devices (iPhone, Android, Raspberry Pi etc) so that benchmarking and profiling can be done on these mobile devices.
  • Revamp the data storage backend of MLModelScope to store various benchmark data (across models, datasets, frameworks, and systems), and enable the use of those data for advanced use cases
  • Deploy and launch the MLModelScope platform on at least two hybrid cloud providers (such as IBM Cloud and Amazon Cloud), and demonstrate their scalability in terms of number of nodes, models, users, and experiments.
  • Revamp the MLModelScope's frontend to make the GUI interface more stable and support customized user login and data management (such as uploading new models and retrieving user specific experiment data). Demonstrate the resilience of the design and implementation against attacks.
  • Build the frontend use cases for showing leaderboards of models and systems in terms of model accuracy and system performance.
  • Demonstrate a system integration API and test the use case of hybrid deployment for any hardware system providers (i.e., any system providers can integrate their own systems into the existing MLModelScope platform for benchmarking)
  • Improve the scalability and robustness of MLModelScope to support large-scale users. Develop the tests for measuring the system response time and throughput under varying levels of load.
  • Build automated test and regression for MLModelScope across models, frameworks, and systems.
  • Write documentation to allow any third-party users to easily deploy MLModelScope either on hybrid clouds or as a standalone version through CLI or API interfaces. Provide support to these users during the contract period.

Technical Skills required

In order to perform the above defined tasks well, we believe the following technical skills are required

  • Familiarity with front-end programming frameworks (such as React) and Javascript
  • Familiarity with GUI design with HCI consideration
  • Familiarity with Hybrid Clouds technologies and deployments
  • Experience in provisioning and deployment of complex software in at least two commercial cloud providers' technologies (such as IBM Cloud, Amazon Cloud)
  • Experience in backend technologies, node.js, mongoDB, RPC, Restful, serveless
  • Experience in Golang, C++, Python, Java
  • Experience in at least one common Machine Learning and Deep Learning framework
  • Experience in designing and deploying large-scale enterprise AI solutions
  • Experience in designing user authentication systems
  • Experience in managing the complexity of large-scale projects
  • Experience in adapting to changing technology landscapes
  • Great communication skills and team work skills
  • Familiarity with the current MLModelScope code base (which can be made available)
- provided by Dice

 
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