Machine Learning Engineer with Security Clearance

Machine Learning Engineer Role The Machine Learning Engineer will be an essential member of Eccalon's Machine Learning R&D 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. Responsibilities: 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. Required Qualifications: -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. Preferred qualifications: -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.