Machine Learning Engineer

EOE Statement Eccalon provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. EEO is the law. Machine Learning Engineer 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. 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 your 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. #J-18808-Ljbffr