Liquid AI, an MIT spin-off, is a foundation model company headquartered in Boston, Massachusetts. Our mission is to build capable and efficient general-purpose AI systems at every scale.
Our goal at Liquid is to build the most capable AI systems to solve problems at every scale, such that users can build, access, and control their AI solutions. This is to ensure that AI will get meaningfully, reliably and efficiently integrated at all enterprises. Long term, Liquid will create and deploy frontier-AI-powered solutions that are available to everyone.
We are seeking a highly skilled ML Engineer to play a critical role in our foundation model development process. The ideal candidate will be responsible for designing, developing, and implementing sophisticated synthetic and real-world data generation strategies that will feed and improve our AI model's training pipeline.
Key Responsibilities
Design and implement comprehensive data generation strategies for foundation model trainingDevelop synthetic data generation techniques that enhance model performance and diversityCurate, clean, and validate large-scale real-world datasetsCreate advanced data augmentation and transformation pipelinesEnsure data quality, ethical considerations, and bias mitigation in data generationDevelop tools and frameworks for reproducible and scalable data generationMonitor and assess the impact of generated data on model performance,
Preferred Skills
Experience with large language models or multimodal foundation modelsKnowledge of differential privacy and data anonymization techniquesExperience with data ethics and bias detectionPublications or research in synthetic data generationUnderstanding of scalable data processing architectures,
Required Qualifications
Design and implement comprehensive data generation strategies for foundation model trainingDevelop synthetic data generation techniques that enhance model performance and diversityCurate, clean, and validate large-scale real-world datasetsCreate advanced data augmentation and transformation pipelinesEnsure data quality, ethical considerations, and bias mitigation in data generationDevelop tools and frameworks for reproducible and scalable data generationMonitor and assess the impact of generated data on model performance