Principal Machine Learning: Generative AI. United States

Description

• Work with large-scale multimodal datasets (text, unstructured and structured data), developing advanced preprocessing, augmentation, and content understanding techniques
• Architect and guide the implementation of scalable data pipelines and architectures
• Perform in-depth requirements analysis, collaborating with team members at different levels and documenting solutions
• Contribute to technical execution by writing well-structured, high-performance code for production ML pipelines
• Set the technical direction by identifying key challenges and defining innovative solutions
• Mentor and guide junior engineers, fostering a culture of technical excellence and knowledge-sharing within the team
• Architect, develop, and optimize production-level ML solutions, focusing on scalability and reliability, while contributing to engineering best practices
• Establish best practices for model experimentation, evaluation, and optimization
• Communicate technical findings effectively, influencing stakeholders through quantitative analysis, qualitative insights, and clear visual presentations

Requirements

• Proven ability to translate theoretical concepts into practical solutions and prototype implementations
• Expertise in deep learning architectures (e.g., Transformers, CNNs, GANs) and modern ML frameworks (e.g., PyTorch, Lightning, Ray)
• Proficiency in parallel and distributed computing techniques, with hands-on experience using platforms like Spark, Ray, or similar distributed systems for large-scale data processing and model training
• You seek innovative solutions to difficult technical problems and iterate quickly on ideas
• Excellent technical writing and communication skills for documentation, presentations, and influencing cross-functional teams
• An MS in Machine Learning, Artificial Intelligence, Mathematics, Statistics, Computer Science, or a related field
• Experience with LLMs and related technologies, including frameworks, embedding models, vector databases, and Retrieval-Augmented Generation (RAG) systems, in production settings
• You are comfortable driving progress in newly forming, ambiguous areas where learning and adaptability are key
• Experience with AWS cloud services and SageMaker Studio for scalable data processing and model development
• You easily collaborate with others and are comfortable with minimal direction
• Background in Architecture, Engineering, or Construction
• 7-10+ years of experience in machine learning engineering or a related field, with a proven track record of leadership and hands-on implementation
• Proven record in developing and deploying high-scale machine learning algorithms in production environments
• Deep understanding of data modeling, system architectures, and processing techniques.
• Extensive experience in data preparation, hyper-parameter selection; acceleration techniques; and optimization methods
• Strong foundation in computer science fundamentals, distributed computing, and algorithmic efficiency
• Ability to work autonomously while effectively collaborating across teams, bridging the gap between research and practical implementation

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance