Machine Learning Engineer

The Machine Learning Engineer will be an essential member of the Research and Development Team, where we engineer large tailor-made systems to solve complex data-related problems from many domains. At Eccalon, the projects we support often require solutions that utilize the latest and the best from Deep Learning/Machine Learning research. We support advanced projects in both data constrained and data rich settings. Qualified candidates should be driven and be able to help craft these systems as a part of our R&D team., * Candidates are expected to be familiar with the motions of a classical Machine Learning workflow, and support the team with some of the following tasks:

• Dataset Creation.
• Data Exploration/Visualization.
• Literature Review.
• Data Wrangling.
• Implementation and Training of Appropriate Models from Literature.
• Characterization of Error in Models.
• Iterative Optimization of Models.
• On the engineering side of development, the Machine Learning Engineer will have the ability to be hands-on by:
• Creating training and preprocessing pipelines for faster experimentation.
• Creating algorithmic modules to interface your Models output with business requirements.
• Integrating their code to a larger codebase.
• Putting your model into production using AWS or GCP.

Do you have experience in Statistics?, Do you have a Bachelor’s degree?, * BS. in Computer Science, or related field.

• 3+ years of professional Software Development experience in Python.
• Mastery of Deep Learning fundamentals and statistics underlying Machine Learning.
• History of software projects putting Machine Learning systems into production in any capacity.
• History of software projects in general.
• Deep personal interest with the complete state of the art in a subfield of Machine Learning Research.
• Ability to work independently, and within a team.
• Ability to communicate effectively with non-technical stakeholders and supervisors.
• Prior project experience combining two or more of the following in a production setting:
• Unsupervised or Semi-supervised Learning.
• Convolutional Architectures.
• Autoencoders.
• Recurrent Architectures for Time-Series Applications.
• Transformer Architectures for Natural Language Processing.
• Generative Adversarial Architectures., * MS. or PhD in Machine Learning, or related field
• Extensive AWS or GCP experience putting scalable Machine Learning systems into production.
• Experience working with extremely high volume / high throughput data in a data lake / data warehousing / training / production environment.
• Has implemented cutting edge methods (e.g. a custom layer) from recent Machine Learning publications / conference proceedings and has done so in PyTorch or Tensorflow.
• Publications in AI/ML journals or conferences.