AI Machine Learning Engineer - Data Annotation

Insight Global is looking for an AI Machine Learning Engineer within Data Annotation to work with one of our tech clients in the Bay Area.

Work alongside NVIDIA’s data annotation platform team, dedicated to addressing our evolving annotation data needs for training and evaluating LLMs. We value expertise in data science paired with a robust data engineering foundation. Together, we'll architect data pipelines, delve into advanced data analysis, and craft models using ML/AI. We seek someone proficient in programming and scripting for comprehensive data manipulation, analysis, and model creation.

If you share a passion for innovation and creating exceptional experiences through the integration and management of large data sets, then you're the one we've been searching for!

This role can pay between $80-100/hour depending on years of experience and skillset.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances.

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• Bachelor’s Degree in Computer Science or related field or equivalent experience.
• Expertise as a Machine Learning Engineer or a similar role, with a consistent record of successfully delivering ML solutions.
• Strong programming skills in languages such as Python. Experience with frameworks like Tensor Flow, PyTorch, or scikit-learn.
• Proficiency in data manipulation, analysis, and visualization using tools like Num Py, pandas, and matplotlib.
• Deep understanding of machine learning algorithms, statistical models, and data structures.
• Familiarity with software development practices and version control systems (e.g., Git).
• Ability to run with experimental design, A/B testing, and evaluation metrics for ML models. Experience in data annotation platforms, synthetic data generation techniques and excellent data analysis skills.
• First‑hand experience deploying and maintaining deep learning systems and services in production at scale.
• Demonstrated skills with large language models, natural language processing and multi-modal models.