Job Description
Are you ready to architect the future of synthetic intelligence? Neural Horizon Labs is pioneering the next generation of cognitive computing with the launch of the revolutionary 2026 Platform. We are looking for a visionary Senior AI Architect to lead the development of scalable, self-improving neural networks that will define the industry standard for the coming decade.
In this pivotal role, you will bridge the gap between theoretical machine learning and production-grade infrastructure. You will work directly with our research team to deploy models that solve complex, real-world problems in predictive analytics and autonomous systems. If you are obsessed with performance, scalability, and pushing the boundaries of what AI can achieve, we want to hear from you.
Why Join Us?
- Work on a proprietary platform that is setting the roadmap for the AI industry.
- Competitive compensation package with equity options.
- Flexible remote-first culture with opportunities for in-person collaboration in San Francisco.
Responsibilities
- Design and architect the core infrastructure for the 2026 Platform, ensuring high availability and low-latency inference.
- Lead the migration of legacy models to our next-generation neural engine, optimizing for GPU utilization and energy efficiency.
- Collaborate with data scientists to translate research prototypes into robust, scalable production APIs.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and innovation.
- Implement rigorous testing and monitoring protocols to ensure model accuracy and system stability.
- Stay ahead of industry trends in Generative AI and Distributed Systems to continuously improve the platform.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field.
- 5+ years of professional experience in AI/ML engineering, with at least 2 years in a lead or architect role.
- Deep expertise in Python, TensorFlow, PyTorch, or JAX.
- Proven experience designing distributed systems and cloud-native architectures (AWS, GCP, or Azure).
- Strong understanding of MLOps, model versioning, and deployment pipelines.
- Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.