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Information Technology 🏢 Full Time ⭐️ Verified

AI Research Scientist - Future Autonomy Systems

Nexus Horizon Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
15 Mei 2026
Deadline
15 Mei 2027

Job Description

Are you ready to architect the intelligent systems of tomorrow?

Nexus Horizon Labs is pioneering the next generation of autonomous decision-making. As we look toward the strategic vision for 2026, we are seeking a visionary AI Research Scientist to lead our Long-term Autonomy division. You will work at the intersection of deep learning, predictive modeling, and ethical AI to build systems that redefine human-machine interaction.

Why Join Us?

  • Work on cutting-edge research with a direct impact on the future of technology.
  • Competitive compensation and equity packages for top-tier talent.
  • Collaborate with a world-class team of engineers and data scientists.

Responsibilities

  • Research and develop novel deep learning architectures tailored for long-term temporal reasoning and autonomy.
  • Collaborate with software engineering teams to translate theoretical models into scalable production systems.
  • Publish high-impact research papers in top-tier conferences (NeurIPS, ICML, ICLR).
  • Design and optimize reinforcement learning algorithms for complex decision-making scenarios.
  • Conduct rigorous evaluation of model performance, bias, and fairness across diverse datasets.

Qualifications

  • PhD or Master's degree in Computer Science, Artificial Intelligence, or a related quantitative field.
  • 5+ years of experience in machine learning research and development.
  • Expert proficiency in Python, PyTorch, and TensorFlow.
  • Strong background in Natural Language Processing (NLP) or Computer Vision (CV).
  • Proven track record of publishing research in reputable academic venues.
  • Demonstrated ability to work independently and drive projects from conception to completion.

Required Skills

Python PyTorch TensorFlow Machine Learning Deep Learning NLP Reinforcement Learning Research

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