Machine Learning Engineer

The role

You will develop data processing pipelines and machine learning solutions for large-scale NLP and LLM applications. You will design, implement, and maintain data-driven models and algorithms.

What you will do

• Developing data processing pipelines and machine learning solutions for large-scale NLP and LLM applications
• Designing and implementing pipelines for processing and analyzing large datasets
• Analyzing and interpreting complex time series data
• Designing, implementing, and maintaining data-driven models and algorithms
• Developing techniques for dataset curation to improve AI training data quality and efficiency
• Building scalable pipelines for filtering, deduplicating, and improving large-scale text datasets
• Collaborating with cross-functional teams to deliver solutions
• Ensuring data quality and integrity throughout all processes
• Utilizing Optical Character Recognition (OCR) technology
• Researching and implementing best practices in data science and machine learning

Required qualifications

• 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 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 to articulate complex data concepts to non-technical stakeholders
• Experience working in a high-output team

Nice to have

• Experience working with large-scale text datasets, NLP pipelines, or data preparation for LLM training
• Experience with text deduplication, dataset filtering, corpus curation, or data distillation

Benefits

• competitive salary
• benefits

About the company

wynd-labs builds infrastructure that delivers massive amounts of web data to companies training AI models. They power and support Grass, a bandwidth-sharing network that operates a massive distributed crawler.