Machine Learning Engineer - Data & Annotation Platforms

Sustainable Talent•Santa Clara, CA

2h•$60 - $95•Onsite

About The Position

Sustainable Talent is partnering with Nvidia a global leader who's been transforming computer graphics, PC gaming, and accelerated computing for over 25 years. We are looking for a Machine Learning Engineer to support our client's team onsite in Santa Clara, CA. This is a full-time (W-2) contract role. We offer competitive pay $60/hr - $95/hr based on factors like experience, education, location, etc. and provide full benefits, PTO, and amazing company culture! As a Machine Learning Engineer, you'll 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! We believe in proactive problem-solving, minimal supervision, and being exceptional teammates who collaborate, think, and learn as one unit. Let's make a difference together!

Requirements

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 TensorFlow, PyTorch, or scikit-learn.
Proficiency in data manipulation, analysis, and visualization using tools like NumPy, 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.

Nice To Haves

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.
Self-motivated with a goal to stay updated on the latest methodologies and machine learning technologies.

Responsibilities

Develop and implement ML models: Design, develop, and deploy scalable machine learning models and algorithms that address complex business challenges
Improve and experiment with new directions and provide ML solutions in data annotation platform.
Build models and algorithms to understand user interest, user intent, and improve content relevancy.
Responsible for end-to-end design and development, starting from requirements gathering with business and engineering partners to deployment to product systems using Agile development methodology.
Architect solutions for complex data platforms, and large scale CI/CD data pipelines using a variety of technologies, relational and non-relational databases, and data warehouse solutions for data-driven marketing and compliance requirements.
Data pre-processing and analysis: Collaborate with data scientists and data engineers to collect, clean, pre-process, and transform large and wide datasets. Conduct exploratory data analysis (EDA) to uncover insights and identify patterns that boost the model performance.
Collaborate with multidisciplinary teams: Collaborate with product engineers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.