Job Description
The Future is Here.
We are Apex Future Labs, a premier research organization defining the technological landscape of the Year 2026 and beyond. We are seeking a visionary Lead AI Architect to architect the next generation of autonomous systems, generative intelligence, and scalable infrastructure. If you are passionate about pushing the boundaries of what is possible in AI and want to leave a legacy in the technology of tomorrow, we want to meet you.
In this role, you will bridge the gap between theoretical AI research and production-grade engineering, ensuring our solutions are robust, scalable, and ethically sound for the global market.
Why Join Us?
- Work on cutting-edge projects that define the 2026 tech ecosystem.
- Competitive compensation package with equity options.
- Flexible remote-first culture with a state-of-the-art office in San Francisco.
- Access to the latest hardware and research tools.
Responsibilities
- Define the 2026 Technical Vision: Lead the architectural strategy for our flagship AI products, ensuring alignment with long-term industry trends and internal goals.
- System Design & Scalability: Design and implement high-availability, distributed AI systems capable of processing petabytes of data.
- MLOps Leadership: Oversee the deployment, monitoring, and maintenance of machine learning models in production environments.
- Team Mentorship: Guide a team of senior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Collaboration: Partner with product managers, researchers, and stakeholders to translate complex requirements into technical solutions.
- Performance Optimization: Continuously analyze and optimize model inference speeds and resource utilization.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related field (or equivalent practical experience).
- Experience: 8+ years of experience in software engineering, with at least 4 years in AI/ML architecture and leadership.
- Technical Skills: Proficiency in Python, TensorFlow, PyTorch, and modern deep learning frameworks.
- Cloud Expertise: Deep understanding of cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Problem Solving: Demonstrated ability to solve complex, ambiguous problems with innovative technical solutions.
- Leadership: Proven track record of managing high-performing engineering teams.