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Information Technology 🏒 Full Time ⭐️ Verified

Senior AI & Machine Learning Architect (2026 Vision)

Nebula Core Systems
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
Live Update
1 Juli 2026
Deadline
1 Jul 2027

Job Description

We are seeking a visionary Senior AI & Machine Learning Architect to lead our research division. At Nebula Core Systems, we are not just building software; we are architecting the foundational intelligence for the technological landscape of 2026 and beyond. You will be at the forefront of Generative AI, Large Language Models (LLMs), and autonomous agent development.

In this high-impact role, you will define the technical strategy for our next-generation platforms, optimizing for scalability, efficiency, and ethical AI deployment. If you are passionate about pushing the boundaries of what is possible with artificial intelligence and want to leave a lasting legacy in the tech industry, we want to hear from you.

Responsibilities

  • Architect Next-Gen AI Systems: Design and implement scalable neural architectures for Generative AI and predictive modeling.
  • Pipeline Optimization: Engineer high-performance data pipelines and inference engines to support real-time applications.
  • Research & Innovation: Stay ahead of industry trends, evaluating and integrating cutting-edge research into production environments.
  • Model Deployment: Manage the end-to-end lifecycle of machine learning models, from training and validation to MLOps and monitoring.
  • Technical Leadership: Mentor a team of engineers, conduct code reviews, and establish best practices for AI development.
  • Collaboration: Partner with product and engineering teams to translate business requirements into robust technical solutions.

Qualifications

  • Education: Master’s or PhD in Computer Science, Mathematics, or a related quantitative field.
  • Experience: 5+ years of professional experience in AI/ML engineering with a focus on deep learning and NLP.
  • Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and Scikit-learn.
  • Cloud Expertise: Strong experience deploying models on AWS, GCP, or Azure using containerization (Docker/Kubernetes).
  • Mathematical Foundation: Solid understanding of linear algebra, calculus, and probability theory.
  • Problem Solving: Demonstrated ability to tackle complex, unstructured problems and deliver innovative solutions.

Required Skills

Python PyTorch TensorFlow AWS GCP Docker Kubernetes Machine Learning Deep Learning Natural Language Processing MLOps

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