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Co-founder and CEO

Co-founder and CTO

Prompt Economy Debate Turns to Scaling Agentic AI

OpenAI keeps raising big and spending big, Oracle gets two new CEOs, and Trump cracks down on H1-B visas

Liquid unveils “Nanos”: Extremely small foundation models that match frontier-model quality—running directly on everyday devices

Liquid AI debuts extremely small, high-performance foundation models for on-device processing

Member of Technical Staff - ML Research Engineer

Liquid AI is a foundation model company spun out of MIT, focused on building efficient AI systems at every scale. The role of Member of Technical Staff - ML Research Engineer involves developing Liquid Vision-Language models and gathering high-quality datasets for training and fine-tuning these models.

Member of Technical Staff - ML Research Engineer, VLM Data

Liquid AI is a foundation model company spun out of MIT, focused on building efficient AI systems at every scale. The role of Member of Technical Staff - ML Research Engineer involves consolidating and generating high-quality text data for various stages of model training, while also maintaining data processing pipelines and gathering datasets.

Member of Technical Staff - ML Research Engineer, Foundation Model Data

Member of Technical Staff - ML Research Engineer, Performance Optimization

Liquid AI is a foundation model company spun out of MIT, focused on building capable and efficient general-purpose AI systems. The role involves writing high-performance GPU kernels for inference workloads and optimizing architectures across various model parameter sizes.

Member of Technical Staff - Applied ML Scientist

Liquid AI is a foundation model company spun out of MIT, focused on building efficient AI systems at every scale. The Applied ML Scientist role involves owning the experimental process, designing structured experiments, and translating customer needs into actionable model improvements, all while collaborating closely with research and customer teams.

Member of Technical Staff - Training Infrastructure Engineer

Liquid AI is a foundation model company spun out of MIT, focused on building efficient AI systems at every scale. The Training Infrastructure Engineer will design and implement high-performance, scalable training infrastructure while collaborating with ML engineers to optimize model architectures and training algorithms.

Member of Technical Staff - ML Inference Engineer

Liquid AI is a foundation model company spun out of MIT, focused on building efficient AI systems at every scale. The role of ML Inference Engineer involves optimizing and productionizing the end-to-end pipeline for GPU model inference, while also facilitating the development of next-generation Liquid Foundation Models.

Member of Technical Staff - ML Inference Engineer, Pytorch

Member of Technical Staff - Applied ML Engineer

Liquid AI, spun out of MIT, is focused on building efficient AI systems at every scale. They are seeking an Applied ML Engineer to optimize and deploy local LLMs, customize ML models for customer needs, and ensure effective deployment in resource-constrained environments.

Member of Technical Staff - Fullstack Engineer

Liquid AI is a foundation model company spun out of MIT, focused on building capable and efficient general-purpose AI systems. The Member of Technical Staff - Fullstack Engineer will architect and implement full-stack solutions, design backend services, and develop deployment infrastructure for ML systems, collaborating closely with Product and ML teams.

Liquid AI is a foundation model company spun out of MIT, focused on building capable and efficient AI systems at every scale. The Member of Technical Staff - Fullstack Engineer will architect and implement full-stack solutions for internal platforms and customer-facing products, while collaborating closely with Product and ML teams to enhance ML deployment workflows.

Liquid AI is a foundation model company spun out of MIT, focused on building efficient AI systems at every scale. The role of Member of Technical Staff - ML Inference Engineer involves optimizing and productionizing GPU model inference pipelines, facilitating the development of next-generation Liquid Foundation Models, and profiling the stack for various serving requirements.