Job Description
Are you ready to shape the future of intelligence? Apex Future Systems is pioneering the 2026 Initiative, a groundbreaking project to architect the next generation of autonomous AI agents. We are seeking a visionary Lead AI Architect to lead our engineering team in building scalable, safe, and super-intelligent systems.
In this role, you won't just maintain legacy codebases; you will define the architectural standards for AGI (Artificial General Intelligence) development. If you are passionate about the intersection of ethics, advanced mathematics, and software engineering, we want to meet you.
Why join us?
- Work on the frontier of AI technology.
- Competitive equity package and top-tier compensation.
- Flexible remote-first culture with a hub in San Francisco.
Responsibilities
- Architectural Leadership: Design and implement the core infrastructure for our 2026 AI models, ensuring high availability, security, and scalability.
- R&D Strategy: Define the research roadmap for LLM fine-tuning, reinforcement learning from human feedback (RLHF), and multi-modal agent workflows.
- Code Optimization: Drive performance engineering to reduce inference latency and optimize training throughput for large-scale distributed systems.
- Team Mentorship: Cultivate a culture of technical excellence, mentoring junior engineers and data scientists in cutting-edge AI techniques.
- Ethical AI Oversight: Implement guardrails and safety protocols to ensure AI outputs align with human values and regulatory standards.
- Collaboration: Partner with product and legal teams to translate complex AI capabilities into user-friendly, compliant applications.
Qualifications
- Education: Ph.D. or Master's degree in Computer Science, Mathematics, Statistics, or a related field.
- Experience: 8+ years of software engineering experience, with at least 4 years specifically focused on Machine Learning and Deep Learning systems.
- Technical Skills: Proficiency in Python, C++, and CUDA; extensive experience with PyTorch or TensorFlow; familiarity with distributed computing frameworks (Kubernetes, Ray, Spark).
- Domain Knowledge: Deep understanding of Transformer architectures, Large Language Models (LLMs), and Reinforcement Learning.
- Soft Skills: Exceptional problem-solving abilities and strong communication skills to articulate complex technical concepts to diverse stakeholders.