Job Description
Are you ready to architect the intelligence of tomorrow? At Apex Neural Systems, we are not just building software for today; we are engineering the foundational infrastructure for the year 2026. We are seeking a visionary Future-Ready AI Architect to lead our research division in San Francisco.
In this high-impact role, you will spearhead the development of next-generation Large Language Models (LLMs) and autonomous agents designed to operate seamlessly in complex, decentralized environments. You will define the technical roadmap that bridges current AI capabilities with the predictive needs of the 2026 horizon.
Why Join Us?
Work with a world-class team of researchers and engineers. We offer competitive equity packages, a fully remote-first culture with hubs in SF, and the autonomy to define how artificial general intelligence evolves.
Responsibilities
- Architect 2026-Ready Systems: Design scalable, fault-tolerant neural network architectures capable of processing multi-modal data streams at petabyte scale.
- Prompt Engineering & Fine-Tuning: Lead initiatives in Reinforcement Learning from Human Feedback (RLHF) to optimize model alignment and safety standards.
- Research & Innovation: Push the boundaries of Generative AI, exploring novel applications in predictive analytics and autonomous decision-making.
- Infrastructure Optimization: Oversee the deployment of models on high-performance GPU clusters, ensuring low-latency inference for real-time applications.
- Technical Leadership: Mentor junior engineers and data scientists, fostering a culture of continuous learning and technical excellence.
- Compliance & Ethics: Implement rigorous guardrails to ensure AI outputs are unbiased, transparent, and compliant with evolving global regulations.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, or a related quantitative field from a top-tier institution.
- Experience: 7+ years of experience in AI/ML engineering, with at least 3 years in a lead or architect role.
- Technical Stack: Deep proficiency in Python, PyTorch, TensorFlow, and experience with major LLM frameworks (Hugging Face, LangChain).
- System Design: Strong understanding of distributed systems, cloud architecture (AWS/GCP), and containerization technologies (Docker, Kubernetes).
- Research Mindset: Proven track record of publishing in top-tier conferences (NeurIPS, ICML) or delivering breakthrough commercial products.
- Future Vision: Ability to anticipate technological trends and translate them into actionable engineering roadmaps.