Machine Learning Engineer

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EEO is the law.

Location:

Detroit, MI – Onsite

Type:
Full-time

Security Clearance:
No clearance required; must be clearable.

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. The projects often require solutions that utilize the latest and 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 your code to a larger codebase.
  • Putting your model into production using AWS or GCP.

• 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.