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Senior AI Research Scientist

QuantumLeap AI
San Francisco
Salary Estimate
USD 180.000 – USD 250.000
Live Update
14 Mei 2026
Deadline
14 Mei 2027

Job Description

Join QuantumLeap AI's groundbreaking research division at the forefront of artificial intelligence innovation. We're seeking a visionary Senior AI Research Scientist to develop transformative machine learning solutions that redefine industry standards. Collaborate with elite researchers in our state-of-the-art San Francisco lab while leveraging our $50M compute infrastructure. This role offers unparalleled opportunities to publish in top-tier journals, lead open-source initiatives, and shape the future of generative AI.

Our engineers solve problems ranging from large-scale LLM optimization to ethical AI frameworks. You'll influence product strategy while maintaining academic rigor through our dual-track research pipeline. Enjoy comprehensive benefits including equity, flexible schedules, and dedicated innovation time.

Responsibilities

  • Lead research initiatives in generative AI, reinforcement learning, and multimodal systems
  • Design and implement novel neural architectures for enterprise-scale applications
  • Collaborate with engineering teams to translate research into production-ready solutions
  • Publish peer-reviewed papers at NeurIPS, ICML, and other top-tier conferences
  • Mentor junior researchers and drive technical strategy for AI product lines
  • Develop ethical frameworks for responsible AI deployment
  • Contribute to open-source initiatives and build thought leadership

Qualifications

  • PhD in Computer Science, Machine Learning, or related field (MS + 5 years experience considered)
  • 3+ years of industry experience deploying production ML systems at scale
  • Expertise in deep learning frameworks (PyTorch/TensorFlow) and distributed training
  • Strong publication record in top-tier ML conferences or journals
  • Proficiency in Python, CUDA, and high-performance computing
  • Demonstrated experience with LLM optimization and fine-tuning
  • Knowledge of MLOps pipelines and cloud deployment (AWS/GCP)
  • Excellent communication skills for technical and non-technical audiences

Required Skills

Machine Learning Deep Learning PyTorch TensorFlow Natural Language Processing Computer Vision LLMs MLOps Research Python CUDA Distributed Computing AI Ethics

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