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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist

Nexus Innovations
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
11 Mei 2026
Deadline
11 Mei 2027

Job Description

Join Nexus Innovations at the forefront of technological evolution as we pioneer solutions for 2026 and beyond. We seek visionary AI Research Scientists to architect transformative machine learning systems that will redefine industries. This role offers unparalleled opportunities to collaborate with Nobel laureates and deploy breakthrough innovations at global scale.

Your mission involves developing novel neural architectures, publishing in top-tier journals, and translating theoretical breakthroughs into real-world applications. We provide competitive equity packages, unlimited research budgets, and flexible work arrangements designed for peak innovation.

Responsibilities

  • Design and implement next-generation AI models for autonomous systems and predictive analytics
  • Lead cross-functional research initiatives in quantum-enhanced machine learning
  • Publish breakthrough research in Nature/Science-indexed journals
  • Develop patentable algorithms for ethical AI deployment
  • Mentor PhD-level researchers in advanced deep learning techniques
  • Collaborate with hardware teams to optimize AI-processor co-design
  • Translate research prototypes into production-grade solutions

Qualifications

  • PhD in Computer Science, Mathematics, or related field with 3+ years postdoc experience
  • Published research in top-tier conferences (NeurIPS, ICML, CVPR)
  • Expertise in transformer architectures, reinforcement learning, and generative models
  • Proficiency in PyTorch/TensorFlow and distributed computing frameworks
  • Strong track record of open-source contributions with 1k+ GitHub stars
  • Experience deploying AI models at cloud-scale (AWS/GCP/Azure)
  • Demonstrated ability to secure $500k+ in research grants

Required Skills

Machine Learning Deep Learning Python PyTorch TensorFlow Quantum Computing Reinforcement Learning Generative AI Distributed Systems Research Publication

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