Job Description
We are on the precipice of a technological paradigm shift. OmniFuture Systems is seeking a visionary Senior AI Architect (2026 Vision) to lead our research division in shaping the next generation of artificial intelligence.
As we approach the 2026 technological milestone, the industry is moving toward Autonomous Agentic AI and Neuromorphic Computing. In this role, you will define the architectural backbone of our platforms, ensuring we remain at the forefront of innovation. You will not just write code; you will define the standards for future scalability, ethics, and performance.
Why Join OmniFuture Systems?
- High-Impact Work: Build the core infrastructure that powers the AI of tomorrow.
- Industry-Leading Compensation: Base salary of $185k-$260k plus performance equity.
- Future-Proofing: Work on projects that are specifically architected for the 2026 roadmap.
- Culture of Excellence: Join a team of elite engineers and researchers in the heart of Silicon Valley.
Responsibilities
- Design and deploy scalable generative AI models optimized for ultra-low latency inference in edge environments.
- Lead architectural decisions for the integration of quantum-ready algorithms into classical neural network pipelines.
- Collaborate with cross-functional teams to translate cutting-edge research into production-ready product features.
- Establish and enforce rigorous best practices for model governance, security, and reproducibility.
- Conduct deep-dive research and prototyping of emerging technologies relevant to the 2026 landscape.
- Mentor senior and junior engineers, fostering a culture of technical excellence and continuous learning.
- Optimize training pipelines to reduce compute costs by 30%+ while improving model accuracy.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
- Minimum of 7 years of professional experience in AI software engineering and research.
- Deep expertise in PyTorch, TensorFlow, or JAX with a portfolio of deployed models.
- Proven track record of deploying Large Language Models (LLMs) and Multi-Agent Systems in production.
- Strong understanding of distributed systems, cloud architecture (AWS/GCP), and containerization (Docker/Kubernetes).
- Experience with MLOps tools such as MLflow, Airflow, or Kubeflow.
- Excellent verbal and written communication skills, with the ability to present complex technical concepts to non-technical stakeholders.