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AI/ML Research Scientist (2026)

Nexus Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
3 Juli 2026
Deadline
3 Jul 2027

Job Description

Join Nexus Labs at the forefront of 2026's technological revolution! We're seeking visionary AI/ML Research Scientists to architect the next generation of intelligent systems. As a pioneer in quantum-enhanced machine learning, you'll transform theoretical breakthroughs into real-world solutions that redefine industries. Our state-of-the-art facility in San Francisco's innovation hub offers unparalleled resources to push the boundaries of what's possible.

Collaborate with Nobel laureates and industry disruptors in an environment where curiosity fuels progress. We provide competitive equity packages, flexible research budgets, and dedicated time for academic publications. Shape the future of AI while maintaining work-life balance through our hybrid-first policy.

Responsibilities

  • Design and implement quantum-ML algorithms for 2026-era computational paradigms
  • Lead cross-functional teams in developing autonomous AI systems with ethical guardrails
  • Author groundbreaking research papers for top-tier AI conferences (NeurIPS, ICML)
  • Translate theoretical models into production-ready solutions with 99.9% reliability
  • Mentor junior researchers in emerging fields like neuromorphic computing
  • Secure $5M+ in annual research funding through grant proposals

Qualifications

  • PhD in Computer Science, Quantum Physics, or related field (or equivalent experience)
  • 5+ years in AI/ML research with peer-reviewed publications
  • Expertise in transformer architectures, federated learning, and quantum computing
  • Proficiency in PyTorch, TensorFlow, and quantum programming frameworks (Qiskit)
  • Proven track record of deploying AI systems at scale (10M+ users)
  • Strong understanding of AI ethics and bias mitigation frameworks
  • Experience securing federal research grants (NSF, DARPA)

Required Skills

AI Machine Learning Quantum Computing Research Deep Learning Ethics PyTorch TensorFlow Qiskit Federated Learning

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