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Machine Learning Engineer - Remote

Company & Context:
At Onclusive, we are passionate about building software that solves important problems in marketing and communications. We partner with the most valuable companies in the world to transform how they use data and technology to drive marketing and brand decisions. Our software has been used to strategize responses to a brand crisis, discover new content and influencers, and gain an edge in the global online business world.
As a Machine Learning Engineer, you will work on creating, tuning, and retraining advanced machine learning models to support analysis at scale on vast amounts of text and analytics data. You will apply your technical knowledge on Onclusive’s billions of online content data points to solve challenging marketing problems. ML Engineers are integral to the success of Onclusive.
  • Prototype, design and build scalable machine learning services and data platforms
  • The system currently processes data on the order of billions of articles across hundreds of languages, hundreds of thousands of requests per second, and terabytes per day
  • Research, design, implement and validate cutting-edge algorithms to analyze diverse sources of data to achieve targeted outcomes
  • Implement, tune, and benchmark ML, AI and NLP techniques for article analysis and attribution
  • Create benchmarks and improve models for anomaly detection, seasonality calculations, and event attribution on time series data from multiple sources
  • Work with technologies like Python, R, Ruby, Scala, Redis, ElasticSearch, Apache Spark, Kubernetes, Docker, etc.
  • This position is not centered around creating internal dashboards and reports (eg. BI, Tableau) and is primarily focused around working with large state of the art models, tuning to our use cases, and deploying at scale.

Key Qualifications
  • BS, MS, or Ph.D in Computer Science, Statistics, or Mathematics or related field and/or equivalent experience in the space
  • Software engineering experience.
  • Familiarity with frameworks and models such as TensorFlow, TF Serving, ONNX Runtime, BERT, Kubeflow, MLflow
  • Interest in applying machine learning techniques in production and at scale