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Selected role

Machine Learning Researcher Anomaly Detection

Hyphen Connect United States US

Live

$72k - $100k · Senior Full-time Posted over 1 year ago 3 min read

Outline

Job Summary

We are currently working with multiple early stage venture backed startups in web 3 who are seeking specialists in Machine Learning Optimisation and Research. Across teams we are seeking candidates with the following qualities:

We are seeking a talented and motivated Machine Learning Researchers with experience on anomaly detection within the Web3 ecosystem. As a Machine Learning Researcher, you will work on advancing the state-of-the-art in machine learning algorithms and methodologies to detect anomalies in Web3 applications such as decentralized finance (DeFi), blockchain analytics, decentralized identity, or other relevant areas. Your primary responsibility will be to develop and apply novel machine learning approaches tailored for anomaly detection in Web3, leveraging techniques such as deep learning, unsupervised learning, or transfer learning. You will collaborate with a multidisciplinary team of researchers and engineers to drive innovation and develop cutting-edge machine learning solutions for anomaly detection in Web3.

Responsibilities

Research and develop state-of-the-art machine learning algorithms and models for anomaly detection within the Web3 ecosystem.
Explore and experiment with various neural network architectures, unsupervised learning techniques, and other machine learning approaches to effectively detect anomalies in Web3 applications.
Design and implement scalable machine learning frameworks and systems that can handle decentralized and privacy-sensitive data sources within Web3.
Collaborate with researchers, data scientists, and engineers to collect and preprocess data from Web3 platforms and networks for training and evaluation of machine learning models for anomaly detection.
Conduct thorough analysis and evaluation of machine learning models to measure their performance, identify limitations, and propose improvements within the Web3 context.
Stay up-to-date with the latest advancements in machine learning research, anomaly detection techniques, and Web3 technologies to propose novel research directions for anomaly detection in Web3 applications.
Publish research findings in top-tier conferences and journals, and contribute to the open-source community by releasing code and tools relevant to machine learning and anomaly detection in the Web3 ecosystem.
Collaborate with cross-functional teams to integrate machine learning solutions for anomaly detection into existing Web3 platforms, protocols, or security systems.
Mentor and collaborate with junior researchers and interns, providing guidance and support on research projects related to anomaly detection in Web3.

Requirements

Ph.D. or Master's degree in Computer Science, Electrical Engineering, or a related field with a focus on machine learning, artificial intelligence, or a relevant domain.
Strong background in machine learning, including deep learning, unsupervised learning, or transfer learning techniques.
Proficiency in programming languages such as Python, JavaScript, or Solidity, along with experience working with machine learning libraries (e.g., TensorFlow, PyTorch) and Web3 frameworks (e.g., Web3.js, ethers.js).
Solid understanding of machine learning algorithms, statistical modeling, and optimization techniques.
Familiarity with Web3 technologies, such as blockchain platforms (Ethereum, Polkadot), decentralized finance (DeFi), decentralized identity (DID), or other relevant areas within the Web3 ecosystem.
Strong analytical and problem-solving skills, with the ability to design and evaluate complex machine learning models and algorithms for anomaly detection in Web3 applications.
Excellent written and verbal communication skills, with the ability to present research findings to both technical and non-technical audiences.
Track record of publishing research papers in top-tier machine learning or Web3-related conferences or journals.
Ability to work independently as well as collaboratively in a dynamic research environment.

Positions are globally remote with some travel requirements.

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