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Information Technology 🏢 Full Time ⭐️ Verified

Senior AI Research Engineer - 2026 Vision

Nexus Future Labs
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
New
Live Update
30 Juni 2026
Deadline
30 Jun 2027

Job Description

We are pioneering the technological landscape of 2026. Nexus Future Labs is seeking a visionary Senior AI Research Engineer to lead our initiative in developing next-generation Generative AI systems. You will be instrumental in architecting the models that will define the future of human-machine interaction. If you are passionate about pushing the boundaries of artificial intelligence and want to solve the complex challenges of the near future, this is your opportunity to build the impossible.

Our Vision for 2026: We are moving beyond simple automation into autonomous, reasoning-capable agents.

Responsibilities

  • Architect & Scale: Lead the research and development of proprietary Large Language Models (LLMs) and multimodal architectures designed for the 2026 landscape.
  • Optimization: Research and implement advanced techniques for model inference optimization, reducing latency and energy consumption in real-time environments.
  • Research Publication: Author high-impact research papers and patents that define industry standards for the coming decade.
  • Collaboration: Bridge the gap between theoretical research and product engineering, ensuring scalable deployment of AI models.
  • Strategic Innovation: Identify emerging trends in AI safety, ethics, and capability to guide the company’s 2026 roadmap.
  • Mentorship: Cultivate a high-performance research culture by mentoring junior engineers and data scientists.

Qualifications

  • Education: Ph.D. or Master’s degree in Computer Science, Machine Learning, Statistics, or a related quantitative field.
  • Experience: Minimum 5+ years of experience in AI/ML research or applied machine learning within a high-growth tech environment.
  • Technical Skills: Deep expertise in Python, PyTorch, or TensorFlow; strong understanding of Transformer architectures and reinforcement learning.
  • Tools: Proven experience with distributed computing frameworks (e.g., Kubernetes, Ray, Spark) for large-scale training.
  • Problem Solving: Ability to tackle ambiguous, open-ended problems with creative, data-driven solutions.
  • Communication: Exceptional ability to translate complex technical concepts for diverse stakeholders.

Required Skills

Python PyTorch TensorFlow Machine Learning Deep Learning NLP LLMs Generative AI Reinforcement Learning Distributed Systems Research

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