About the job
Sustainable Talent is excited to collaborate with Nvidia, a global leader in the transformation of computer graphics, PC gaming, and accelerated computing for over 25 years. We are searching for a Machine Learning Engineer to join our client’s team on-site in Santa Clara, CA.
This full-time (W-2) contract position offers competitive compensation ranging from $60/hr to $95/hr, based on experience, education, and location, along with comprehensive benefits, paid time off, and a vibrant company culture!
As a Machine Learning Engineer, you will collaborate with NVIDIA’s data annotation platform team to meet our evolving annotation data needs for training and evaluating large language models (LLMs). We seek candidates who possess a blend of data science expertise and a solid data engineering foundation. You will design data pipelines, conduct advanced data analysis, and develop models using machine learning and artificial intelligence techniques. If you are passionate about innovation and excel at managing and integrating large datasets, you might be the ideal fit! Our team values proactive problem-solving and teamwork—together, we aim to make a significant impact!
Key Responsibilities:
- Design and implement ML models: Build scalable machine learning models and algorithms to tackle complex business challenges.
- Innovate and experiment with new directions to enhance ML solutions within our data annotation platform.
- Develop models and algorithms to analyze user interest and intent, enhancing content relevancy.
- Oversee the entire design and development process, starting from requirements gathering with business and engineering partners to deployment using Agile methodologies.
- Architect solutions for complex data platforms and large-scale CI/CD data pipelines, utilizing various technologies, including both relational and non-relational databases and data warehouse solutions for data-driven marketing and compliance.
- Data preprocessing and analysis: Work closely with data scientists and data engineers to collect, clean, preprocess, and transform extensive datasets. Conduct exploratory data analysis (EDA) to extract valuable insights.