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Senior AI/ML Architect (2026 Vision)

Apex Intelligence Systems
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
New
Live Update
15 Mei 2026
Deadline
15 Mei 2027

Job Description

We are building the infrastructure for 2026. At Apex Intelligence Systems, we are defining the future of Generative AI and Autonomous Systems. We are looking for a visionary Senior AI/ML Architect to lead our research and engineering teams in developing scalable, ethical, and next-generation neural architectures. If you want to work on problems that will define the technological landscape of the next decade, this is your opportunity.

Why join us?

  • Shape the roadmap for AGI (Artificial General Intelligence) by 2026.
  • Work with state-of-the-art hardware and cloud infrastructure.
  • Competitive equity package and top-tier benefits.

Responsibilities

  • Design and implement scalable machine learning pipelines for large-scale language models and multimodal systems.
  • Lead architecture reviews to ensure code quality, scalability, and performance optimization.
  • Collaborate with cross-functional teams to integrate AI models into production environments.
  • Define technical strategies for long-term AI research initiatives relevant to the 2026 market.
  • Mentor junior engineers and data scientists, fostering a culture of innovation and continuous learning.
  • Ensure ethical AI practices and compliance with industry regulations.

Qualifications

  • Ph.D. or Master’s degree in Computer Science, Mathematics, or a related field.
  • 10+ years of experience in machine learning, deep learning, or artificial intelligence.
  • Expert proficiency in Python, PyTorch, TensorFlow, or JAX.
  • Strong experience with distributed systems and MLOps (Kubernetes, AWS SageMaker, or GCP AI Platform).
  • Proven track record of deploying production-level AI models.
  • Excellent communication skills with the ability to translate complex technical concepts for diverse audiences.

Required Skills

Python Machine Learning Deep Learning AI Architecture MLOps PyTorch TensorFlow Kubernetes AWS GCP Distributed Systems

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