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Member of Technical Staff - Foundational Model Data

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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 Member of Technical Staff, Foundation Model Data to play a critical role in our foundation model development process. This role focuses on consolidating, gathering, and generating high-quality text data for pretraining, midtraining, SFT, and preference optimization.

Key Responsibilities

Create and maintain data cleaning, filtering, selection pipeline than can handle >100TB of data.
Watch out for the release of public dataset on huggingface and other platforms.
Create crawlers to gather datasets from the web where public data is lacking.
Write and maintain synthetic data generation pipelines.
Run ablations to assess new dataset and judging pipelines.

Required Qualifications

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

Preferred Qualifications

M.S. or Ph.D. in Computer Science, Electrical Engineering, Math, or a related field.
Experience fine-tuning or customizing LLMs.
First-author publications in top ML conferences (e.g. NeurIPS, ICML, ICLR).
Contributions to popular open-source projects.

Average salary estimate

$125000 / YEARLY (est.)

min

max

$100000K

$150000K

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What You Should Know About Member of Technical Staff - Foundational Model Data, Liquid AI

At Liquid AI, an exciting MIT spin-off located in Boston, Massachusetts, we are on a mission to build capable and efficient general-purpose AI systems at every scale. We're currently seeking a highly skilled Member of Technical Staff - Foundational Model Data to join our dynamic team. In this pivotal role, you will focus on consolidating and generating high-quality text data crucial for the pretraining, midtraining, supervised fine-tuning, and preference optimization stages of model development. Your responsibilities will include creating and maintaining robust data pipelines to handle massive datasets exceeding 100TB, keeping an eye on public dataset releases on platforms like Hugging Face, and even crafting web crawlers to gather additional data when necessary. You will write and improve synthetic data generation pipelines and run ablation studies to evaluate new datasets and evolving pipeline processes. We require someone with experience levels ranging from a B.S. and five years, to a Ph.D. with at least one year of experience, alongside a strong background in dataset engineering. Familiarity with machine learning frameworks, particularly with large language models, is essential, along with robust programming skills in Python. Join us at Liquid AI, where your contributions will support our vision of making frontier-AI-powered solutions accessible to everyone across various sectors!

Frequently Asked Questions (FAQs) for Member of Technical Staff - Foundational Model Data Role at Liquid AI

What are the responsibilities of a Member of Technical Staff - Foundational Model Data at Liquid AI?

As a Member of Technical Staff - Foundational Model Data at Liquid AI, your key responsibilities will include establishing and managing extensive data cleaning and filtering pipelines capable of handling over 100TB of data. You'll also monitor public dataset releases on platforms like Hugging Face and develop web crawlers to source datasets when public data availability is insufficient. Additionally, you'll maintain synthetic data generation pipelines and perform ablation studies to assess new datasets and pipelines.