Machine Learning Engineer, CodaMetrix

• Master's degree in software engineering, computer science or a related technical field with significant course or project work in machine learning, artificial intelligence, or data science. Candidates with a bachelor’s degree with 4 or more years professional software development experience will be considered
• Minimum of two years experience in professional software development
• Fluent with software development best practices, including version control, documentation, testing and CI/CD (Jenkins, Github Actions, Terraform)
• Extensive experience with machine learning approaches and an understanding of the analysis and testing processes of machine learning algorithms. Experience with natural language processing (NLP) is a plus
• Must have experience with cloud computing platforms. Experience with AWS (S3, EC2, etc) is a plus
• Experience with SQL and NoSQL Databases. Experience with SparkSQL is a plus
• Proficiency with Python. Experience with Java and/or Scala is a plus
• Proficiency in working within a Linux ecosystem and extensive experience with Docker
• Experience with Spark, particularly Databricks, is a plus

Desirable

• As a Machine Learning Engineer II with CodaMetrix you will help with translating proof-of-concept ideas to product grade solutions. You will work closely with data scientists, product owners, and backend engineers to gather requirements and understand performance criteria to deliver solutions that bring our AI-driven robust and scalable products to market. The Machine Learning Engineer II reports to the Director of Architecture
• Participate in all team activities, including agile ceremonies and reviews of other engineers designs and code
• Build and maintain the ML infrastructure used by the ML Team and used in production
• Process, transform, and enrich incoming data in order to be compatible with our ML infrastructure and for use by the ML Team
• Develop pipelines and build customer models in support of production
• Establish processes for managing the transitions from proof-of-concept to production
• Promote best practice software development principles
• Collaborate with machine learning, engineering, product development, professional services, DevOps, Data Intelligence, and coding quality assessment teams, for development & deployment of new machine learning techniques and follow deployments, tracking issues, and successes