About the job
Sustainable Talent is collaborating with Nvidia, a pioneering leader in the world of computer graphics, PC gaming, and accelerated computing for over 25 years. We are in search of a Machine Learning Engineer specializing in AI Safety & Security to join our client’s dynamic team located in Santa Clara, CA, with options for remote or hybrid work.
This full-time (W-2) contract position offers competitive compensation ranging from $90/hr to $130/hr, depending on factors such as experience, education, and location, alongside comprehensive benefits, paid time off (PTO), and a vibrant company culture!
As a Machine Learning Engineer, you will collaborate closely with NVIDIA’s research and engineering teams, focusing on AI Safety for large language models (LLMs), including multilingual, multimodal, and reasoning models. We highly value a strong background in data science complemented by solid data engineering skills. This role is primarily dedicated to assessing and enhancing the safety and inclusivity of our LLM models on a scalable level. We are seeking candidates skilled in programming and scripting for detailed data manipulation, analysis, and model fine-tuning. We believe in proactive problem-solving, working with minimal supervision, and fostering exceptional teamwork to make a meaningful impact together!
Key Responsibilities:
- Design and develop datasets and moderator models for evaluating LLMs and end-to-end systems focused on content safety and machine learning fairness.
- Create datasets for training LLMs using supervised fine-tuning (SFT) and reinforcement learning (RL) techniques, targeting content safety, ML fairness, security, and more.
- Research and implement state-of-the-art techniques for bias detection and mitigation in LLMs and related systems.
- Establish and monitor key metrics for responsible LLM behavior and usage.
- Adhere to best practices around automation, monitoring, scalability, and safety.
- Contribute to our repositories and develop safety tools to enhance the effectiveness of machine learning teams.
- Engage in data pre-processing and analysis: Work with data scientists and data engineers to gather, clean, pre-process, and transform extensive datasets.
- Conduct exploratory data analysis (EDA) to uncover insights and identify patterns that enhance model performance.
- Collaborate with multidisciplinary teams: Work alongside product engineers, data scientists, and analysts to comprehend business requirements and translate them into actionable solutions.

