Machine Learning Engineer
Wynd Labs provides internet-scale web data products for AI labs, business intelligence, and multimodal research. Its offerings include real-time search results, multimodal data pipelines, and curated datasets.
Funding
About Wynd Labs
Wynd Labs delivers structured web data at scale for AI development and research. Its product suite includes a Search API for real-time JS-rendered search engine results, multimodal data pipelines spanning video, text, images, and audio, and curated datasets for training and evaluation. Data can be enriched, filtered, and delivered through APIs, direct download, or cloud storage.
Skills
About the Role
You will develop, fine-tune, and deploy large language models for NLP tasks such as text generation, summarization, translation, and sentiment analysis. You will design and implement scalable data processing and analysis pipelines, analyze complex time series data, and apply OCR to extract and normalize text. You will build, maintain, and improve data-driven models and algorithms, ensure data quality and integrity, collaborate with cross-functional teams to deliver solutions, and continuously research and adopt best practices in machine learning and data science.
Requirements
- Bachelor’s, Master’s, or Doctoral degree in Data Science, Computer Science, Statistics, or a related field
- Minimum of 3 years of work or research experience 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
- Good communication skills and ability to articulate complex data concepts to non-technical stakeholders
- Experience working in a high output team and thriving in a fast-paced startup environment
Responsibilities
- Develop, fine-tune, and deploy LLMs for NLP tasks
- Design and implement data processing and analysis pipelines
- Analyze time series data and provide actionable insights
- Design, implement, and maintain data-driven models and algorithms
- Ensure data quality and integrity across processes
- Implement OCR solutions to extract and normalize text
- Collaborate with cross-functional teams to deliver data solutions
- Research and apply best practices in machine learning and data science
- Contribute to development and improvement of internal data processing tools and infrastructure
Benefits
- Remote work
- Equity
- Benefits package
