| Job Position | Company | Posted | Location | Salary | Tags |
|---|---|---|---|---|---|
CERE NETWORK | Remote |
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Cere Network | Europe |
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This job is closed
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About Cere Network
Since its launch in 2019, the Cere team has consistently anticipated the difficulties that the present systems would encounter, now highlighted by the swift advancements of AI and the accompanying surge in data. With companies using multiple vendors causing data fragmentation that complicates AI integration, Cere is presenting itself as an objective, open-source solution, with a clear vision: All data should be decentralized. Unequivocally.
Cere is backed by the world’s largest institutions and projects, including Binance Labs, Republic Labs, and Polygon. For more info, see: www.cere.network
Our Ethos
At Cere, we're not just hiring for roles; we're seeking a specific blend of qualities. We value those who excel in our fast-paced environment, embracing methodical, simulation-driven development and a first-principle thinking approach.
Our team members uphold high standards, discipline and a growth mindset that fuels ongoing learning and process refinement. Exceptional written communication is a must, as we rely on tools like Notion, Slack, and Wiki to ensure organized, transparent collaboration.
We prioritize autonomy and goal orientation, fostering a culture of accountability and transparency. Our ecosystem thrives on supportive, challenge-driven teamwork, a driving force behind Cere’s rapid innovation.
We're drawn to individuals with character, high standards, and the ability to build and optimize efficient habits. A growth mindset and commitment to collective success underscore our team dynamics as we aim to nurture a larger ecosystem of collaboration and progress.
We're more than just a collection of job titles - we're a team of high-achieving, multicultural professionals based around the globe. With offices in cities like San Francisco, New York, Warsaw, Amsterdam, and Berlin, we provide a flexible, remote working environment rooted in a specific ethos that each one of us embodies. Our teams often travel to meet in person every 1-2 months, enriching our remote interactions and cultivating the bonds that make our work productive and enjoyable.
Responsibilities
In this pivotal role at Cere AI, the AI/ML Scientist will be responsible for spearheading a range of initiatives aimed at bolstering data security and enhancing the capabilities of Large Language Models (LLMs). You will co-architect cryptographic solutions with a focus on ZKproof technologies, collaborate closely with the machine learning community to integrate these cryptographic features into LLMs, and develop secure protocols for confidentially sharing trained models. Your expertise will also be crucial in conducting security audits to identify vulnerabilities and in providing secure APIs for third-party integrations. Additionally, you will lead research on emerging cryptographic technologies and offer expert advice to data owners on assessing model credibility while serving as a subject matter expert in both customer and internal settings.
- Collaborate with the machine learning engineers community to integrate cryptographic features into existing and future Large Language Models (LLMs).
- Develop secure protocols that allow for the confidential sharing of trained models between data analysts and data owners.
- Perform comprehensive security audits to identify and address vulnerabilities in LLM architectures and cryptographic implementations.
- Work with engineering teams to provide secure APIs for third-party integrations, facilitating safe interactions between data owners and analysts.
- Lead research initiatives to explore emerging cryptographic technologies, such as Homomorphic Encryption, Multi-Party Computation, and Secure Enclaves, that could enhance the security of our platform.
- Offer expert advice to data owners on assessing the credibility and effectiveness of trained models in the marketplace.
- Serve as a cryptography subject matter expert in customer engagements, internal training sessions, and industry events.
Qualifications
The ideal candidate will hold a Ph.D. with a focus on cryptography and machine learning and have at least 5 years of relevant experience. You should be skilled in, Large Language Models and Natural Language Processing. Proficiency in programming languages like Python, excellent problem-solving abilities, and strong communication skills are also essential.
- Ph.D. in Computer Science, Mathematics, or a related field with a focus on machine learning.
- Experience with Large Language Models, their applications, and associated security risks.
- Strong proficiency in Natural Language Processing (NLP), including familiarity with the latest research and techniques.
- Familiarity with machine learning frameworks like TensorFlow or PyTorch.
- Exceptional problem-solving abilities and critical thinking skills for navigating complex systems.
- Excellent communication skills, capable of translating complicated topics in an understandable manner for a broad audience.
- Demonstrated experience contributing to or leading cryptography-focused projects in a professional setting.
Nice to have
- Expertise in Zero-Knowledge Proofs (ZKproof) and associated cryptographic technologies like Homomorphic Encryption and Secure Multi-Party Computation.
- Programming skills in languages such as Python, Rust or Golang
What is Zero-knowledge?
Zero-knowledge is a concept in cryptography that allows two parties to exchange information without revealing any additional information beyond what is necessary to prove a particular fact
In other words, zero-knowledge is a way of proving something without actually revealing any details about the proof
Here are some examples of zero-knowledge:
- Password authentication: When you enter your password to log into an online account, the server doesn't actually know your password. Instead, it checks to see if the hash of your password matches the stored hash in its database. This is a form of zero-knowledge because the server doesn't know your actual password, just the hash that proves you know the correct password.
- Sudoku puzzles: Suppose you want to prove to someone that you've solved a particularly difficult Sudoku puzzle. You could do this by providing them with the completed puzzle, but that would reveal how you solved it. Instead, you could use a zero-knowledge proof where you demonstrate that you know the solution without actually revealing the solution itself.
- Bitcoin transactions: In a Bitcoin transaction, you prove that you have ownership of a certain amount of Bitcoin without revealing your private key. This is done using a zero-knowledge proof called a Schnorr signature, which allows you to prove ownership of a specific transaction output without revealing the private key associated with that output.
- Secure messaging: In a secure messaging app, you can prove to your contacts that you have access to a shared secret without revealing the secret itself. This is done using a zero-knowledge proof, which allows you to prove that you have access to the secret without actually revealing what the secret is.