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Senior AI Engineer - Horizon 2026

Horizon 2026 Technologies
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
25 Mei 2026
Deadline
25 Mei 2027

Job Description

Are you ready to shape the future of artificial intelligence? Horizon 2026 Technologies is seeking a visionary Senior AI Engineer to lead our next-generation neural architecture projects. We are not just building software; we are architecting the cognitive systems that will define the next decade of human-machine interaction.

In this role, you will be at the forefront of innovation, working with state-of-the-art Large Language Models (LLMs) and generative AI frameworks. You will collaborate with a world-class team of researchers and engineers to deliver scalable, ethical, and high-performance AI solutions that impact millions of users globally.

Why join us?

  • Competitive compensation and equity packages.
  • Access to the latest hardware for AI training and inference.
  • A remote-first culture that prioritizes work-life balance.

Responsibilities

  • Lead Architecture Design: Design and implement scalable, distributed machine learning systems that can handle petabyte-scale data processing.
  • Model Optimization: Fine-tune and optimize transformer models for real-time inference, reducing latency while maximizing accuracy.
  • R&D Leadership: Conduct cutting-edge research in NLP, computer vision, or reinforcement learning to stay ahead of industry trends.
  • Code Quality & Standards: Establish best practices for code review, documentation, and CI/CD pipelines within the AI engineering team.
  • Cross-Functional Collaboration: Partner with product managers and designers to translate complex technical concepts into user-centric AI features.

Qualifications

  • Education: MS or PhD in Computer Science, Mathematics, or a related field (PhD preferred).
  • Experience: 5+ years of professional experience in machine learning engineering or applied AI research.
  • Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX. Deep understanding of neural network architectures.
  • Tools: Experience with MLOps tools (Kubeflow, MLflow) and cloud platforms (AWS, GCP, or Azure).
  • Problem Solving: Demonstrated ability to tackle complex, unstructured problems and deliver robust solutions.

Required Skills

Python Machine Learning Deep Learning PyTorch TensorFlow NLP MLOps AWS Cloud Computing LLMs

Ready to Take This Challenge?

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