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 Machine Learning Engineer, Dataset Engineering to play a critical role in our foundation model development process. This role focuses on designing, implementing, and optimizing large-scale dataset pipelines to enhance model training. The ideal candidate will have extensive experience in machine learning, data curation, and large-scale data processing frameworks. Key Responsibilities

Design, develop, and maintain scalable data pipelines for large-scale multimodal and text-based foundation model training.

Curate, clean, and validate diverse real-world datasets, ensuring high data quality and relevance.

Develop and optimize data preprocessing models for automated cleaning, augmentation, and filtering.

Implement robust data evaluation and benchmarking strategies to assess dataset quality and model impact.

Develop scalable tools and frameworks for data ingestion, transformation, and annotation.

Develop custom synthetic data generation techniques.

Optimize dataset storage and retrieval strategies for efficient large-scale training.

Work closely with research teams to integrate data improvements into model training workflows.

Prototype and iterate on human-in-the-loop solutions to enhance dataset quality.

Required Qualifications

Experience Level: B.S. + 5 years experience or M.S. + 3 years experience or Ph.D. + 1 year of experience.

Relevant Experience: Some combination of at least 2 of the following across research and engineering is ideal: Software Engineering, Data Engineering, Machine Learning Engineering, and Research.

Data Engineering: Expertise in data curation, cleaning, augmentation, and synthetic data generation techniques.

Machine Learning Expertise: Ability to write and debug models in popular ML frameworks, and experience working with LLMs.

Software Development: Strong programming skills in Python, with an emphasis on writing clean, maintainable, and scalable code.

Cloud and Infrastructure: Experience with distributed computing frameworks and cloud storage solutions.

Preferred Qualifications

M.S. or Ph.D. in Computer Science, Electrical Engineering, Math, or a related field.

Experience fine-tuning or customizing LLMs or multimodal foundation models.

First-author publications in top ML conferences (e.g. NeurIPS, ICML, ICLR).

Contributions to popular open-source projects.