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Machine Learning Researcher
4 months ago
This company stands out in the tech world by offering a groundbreaking decentralized compute platform that enables state-of-the-art machine learning research. Utilizing distributed and heterogeneous computing resources worldwide, this platform breaks away from traditional, costly models like AWS and Azure. By tapping into underutilized global compute resources, the platform makes advanced AI research more accessible and efficient, benefiting a wide array of participants from startups to data centers.
About the Role
As a Machine Learning Researcher, your contributions will be crucial in shaping the future of machine learning. This role involves not only developing highly distributed models over a uniquely decentralized infrastructure but also exploring new neural network architectures that push the boundaries of what is possible in AI.
What We Can Offer You
Full Autonomy : Take ownership of projects without the need for middle management. Remote Flexibility : Full-time remote work allows you to work from anywhere Competitive Compensation : Receive a competitive salary along with equity and token shares Professional Growth : Exposure to revolutionary projects in AI and opportunities for professional development. Health and Wellness : Comprehensive health, vision, and dental plans including dependents.
Qualifications
Advanced Degree in Computer Science, Artificial Intelligence, or related field Demonstrable expertise with a portfolio of projects or published papers in significant journals or conferences such as NeurIPS, ICML, AAAI, or similar. Understanding of and experience with distributed computing environments and their application in machine learning. Ability to design, implement, and iterate on new neural network architectures. Strong coding skills in Python, and familiarity with AI frameworks like TensorFlow or PyTorch. Experience with Heterogeneous Computing Hands-on Experience with Large-Scale Machine Learning
Key Responsibilities
Develop and train machine learning models on decentralized and heterogeneous infrastructures. Design and test novel model architectures aimed at enhancing byzantine tolerance in trustless settings. Publish and collaborate extensively, contributing to leading AI conferences and journals. Provide engineering support, ensuring best practices in reproducible training and documentation. Engage with the wider community through publishing and discussing new cutting-edge research via technical papers.
Keywords: Distributed Machine Learning, Heterogeneous Computing, Large-Scale Machine Learning, Deep Learning, Neural Networks, Decentralized AI, LLM, Large Language Models, NLP, NeurIPS, ICML, ICLR, JMLR
