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.
- 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.
- 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.
Basic 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.
- 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.
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