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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist - 2026 Vision

Nexus Dynamics
Austin
Estimated Salary
USD 160.000 – USD 220.000
New
Live Update
2 Juli 2026
Deadline
2 Jul 2027

Job Description

Join Nexus Dynamics at the forefront of technological evolution as we pioneer solutions for 2026 and beyond. We're seeking an exceptional AI Research Scientist to architect next-generation machine learning systems that will redefine industries. This role offers unparalleled opportunities to work with quantum computing prototypes, neural network architectures, and predictive analytics models that will shape our digital future.

Our Austin hub combines cutting-edge R&D with a vibrant tech ecosystem, offering competitive compensation, flexible work arrangements, and comprehensive benefits including equity in our fast-growing unicorn startup.

Responsibilities

  • Lead research initiatives in generative AI and autonomous systems for 2026 market readiness
  • Develop novel neural network architectures optimizing for quantum computing environments
  • Collaborate with cross-functional teams to implement production-ready ML solutions
  • Author peer-reviewed publications and patents in emerging AI domains
  • Mentor junior researchers and establish best practices for AI ethics
  • Secure external funding through NSF and DARPA grant applications
  • Present findings at premier conferences like NeurIPS and ICML

Qualifications

  • PhD in Computer Science, AI, or related field with 3+ years industry experience
  • Published research in top-tier AI/ML conferences (NeurIPS, ICML, CVPR)
  • Expertise in PyTorch/TensorFlow and distributed computing frameworks
  • Proven track record of deploying scalable ML systems at enterprise scale
  • Strong background in reinforcement learning and multi-agent systems
  • Experience with quantum computing APIs (Qiskit, Cirq) preferred
  • Excellent communication skills with ability to translate complex concepts

Required Skills

Machine Learning Neural Networks Quantum Computing PyTorch TensorFlow Reinforcement Learning Distributed Systems Research Ethics

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