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Senior AI & Future Systems Architect (2026 Readiness)

Apex Neural Systems
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
Live Update
24 Mei 2026
Deadline
24 Mei 2027

Job Description

The Future is Now. Join Apex Neural Systems and Architect the Infrastructure of 2026.

We are on a mission to deploy the world's most advanced artificial intelligence architectures. As we prepare for the massive scalability required by 2026, we need a visionary leader to build the backbone of our next-generation neural networks. You won't just be maintaining legacy systems; you will be defining the standards for the future of computing.

At Apex, we value innovation, speed, and radical transparency. You will work directly with our research team to bridge the gap between theoretical AI models and production-grade software.

Responsibilities

  • Design and implement scalable, high-throughput AI infrastructure capable of handling petabyte-scale data processing for 2026 workloads.
  • Architect fault-tolerant systems using Kubernetes and microservices to ensure 99.99% uptime for critical AI inference engines.
  • Collaborate with data scientists to optimize model training pipelines, reducing latency and improving inference accuracy.
  • Lead code reviews and establish best practices for security, performance, and scalability within the engineering team.
  • Research and evaluate emerging technologies (Quantum Computing interfaces, Edge AI) to prepare our roadmap.
  • Mentor junior developers and engineers, fostering a culture of continuous learning and technical excellence.

Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related technical field.
  • 8+ years of experience in software engineering, with at least 4 years focused on AI infrastructure or high-scale backend systems.
  • Expert proficiency in Python, C++, and Go, with deep knowledge of frameworks like TensorFlow, PyTorch, or JAX.
  • Proven experience designing distributed systems and cloud-native architectures on AWS, GCP, or Azure.
  • Strong understanding of MLOps, containerization (Docker, Kubernetes), and CI/CD pipelines.
  • Excellent problem-solving skills and the ability to thrive in a fast-paced, ambiguous startup environment.

Required Skills

Python C++ TensorFlow PyTorch Kubernetes AWS MLOps Distributed Systems Machine Learning Architecture

Ready to Take This Challenge?

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