Remote Aws Machine Learning Jobs in California (NOW HIRING)

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- AI Safety & Security to support our client's team based out of in Santa Clara, CA with remote/ hybrid work options. 

This is a full-time (W-2) contract role. We offer competitive pay $90/hr - $130/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 research and engineering teams, focused on AI Safety for LLMs, including multi-lingual, multi-modal, and reasoning models.  We value expertise in data science paired with a robust data engineering foundation.   This role is directed at assessing, and improving the safety and inclusivity of our LLM models in a scalable fashion.  We seek someone proficient in programming and scripting for comprehensive data manipulation, analysis, and model fine-tuning.  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!

What you'll be doing:

• Develop datasets and moderator models for evaluating LLM models and end-to-end systems for Content Safety, ML Fairness. These LLM models can be txt-to-txt or multimodal-to-txt.
• Develop datasets for training LLM models with SFT and RL techniques, for Content Safety, ML Fairness, Security and more.
• Research and implement cutting-edge techniques for bias detection and mitigation in LLMs and systems.
• Define and track key metrics for responsible LLM behavior and usage.
• Follow the best practices of automation, monitoring, scale, safety.
• Contribute to our repositories and develop safety tools to help ML teams be more effective.
• 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.

What we need to see:

• Bachelor's or Master's Degree in Computer Science or related field or equivalent experience.
• 2+ years of work experience as a Machine Learning Engineer or Deep Learning Scientist 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 and pandas.
• Deep understanding of machine learning algorithms, statistical models, and data structures.
• Familiarity with software development practices and version control systems (e.g., Git).
• Good at problem solving and analytical ability.
• Excellent collaboration and communication skills.

Ways to stand out from the crowd:

• Experience with GenAI Security including Prompt Injection Stability, Model Extraction, Confidentiality/Data Extraction, Integrity, Availability and Adversarial Robustness.
• Experience with one or more of the following areas within Content Safety: Hate/Harassment, Sexualized, Harmful/Violent, or other specific areas from your application.
• Experience with alignment/fine-tuning of LLMs - including regular LLMs as well as VLMs  (Vision Language Model) or any-to-text
• Experience with multimodal and/or multilingual Content Safety, legal and regulatory compliance.
• Passion for AI and a demonstrated commitment to advancing the field through innovative research, prior scientific research and publication experience.

Sustainable Talent is a M/F+, disabled, and veteran equal employment opportunity and affirmative action employer.