Homeoffice AI/ML Engineer

Calyptus · United States Of America · Remote

About the job

This role is with a Calyptus partner.

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The Role.

We are looking for a Machine Learning Engineer who is skilled and has significant experience in developing machine learning models. You will join a small, innovative team and lead efforts to advance our capabilities, drive model development, and support our vision for a future where our organization is transformative in the evolution of the internet.

Who You Are.
  • Bachelor’s, Master’s, or Doctoral degree in Data Science, Computer Science, Statistics, or a related field.
  • A minimum of 3 years of work or research experience dealing with large datasets.
  • Strong coding skills in Python or other object-oriented programming languages.
  • Graduate-level knowledge of statistics, including but not limited to hypothesis testing, regression analysis, and probability.
  • Excellent work ethic and the ability to thrive in a fast-paced startup environment.
  • Strong problem-solving skills and attention to detail.
  • Good communication skills, with the ability to articulate complex data concepts to non-technical stakeholders.
  • Experience working in a high-output team.

What You'll Be Doing.
  • Developing, fine-tuning, and deploying LLMs for various NLP tasks such as text generation, summarization, translation, and sentiment analysis.
  • Designing and implementing pipelines for processing and analyzing large text datasets.
  • Analyzing and interpreting complex time series data to provide actionable insights and solutions.
  • Designing, implementing, and maintaining data-driven models and algorithms.
  • Collaborating with cross-functional teams to understand data needs and deliver timely solutions.
  • Ensuring data quality and integrity throughout all processes.
  • Utilizing Optical Character Recognition (OCR) technology to convert different types of documents into editable and searchable data.
  • Continuously researching and implementing best practices in data science and machine learning.
  • Contributing to the development and improvement of internal data processing tools and infrastructure.

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