Job Description
We are seeking a visionary Senior AI Architect to lead the technical vision for Project 2026, our next-generation generative AI initiative designed to redefine human-machine interaction. As a key player in our elite R&D division, you will be responsible for architecting scalable, high-performance neural networks and guiding a team of top-tier engineers.
In this role, you won't just build models; you will shape the ethical framework and infrastructure that powers the future of enterprise intelligence. If you are passionate about pushing the boundaries of what is possible with Large Language Models (LLMs) and computer vision, this is your opportunity to leave a lasting legacy.
Why join us?
- Work on cutting-edge technology that sets the standard for the industry.
- Competitive compensation package with equity options.
- Flexible remote-first policy with premium office amenities in downtown San Francisco.
Responsibilities
- Architect and implement scalable distributed machine learning pipelines for Project 2026, ensuring low-latency inference and high throughput.
- Lead the research and development of proprietary algorithms, focusing on Transformer architectures and reinforcement learning.
- Collaborate closely with product managers and data scientists to translate business requirements into technical blueprints.
- Establish best practices for model training, validation, and deployment, ensuring high standards of code quality and documentation.
- Mentor junior engineers and data scientists, fostering a culture of innovation and continuous learning.
- Evaluate and integrate third-party AI libraries and hardware accelerators to optimize system performance.
Qualifications
- Master’s or PhD degree in Computer Science, Mathematics, or a related technical field.
- Minimum of 8 years of experience in machine learning, deep learning, or artificial intelligence architecture.
- Proven expertise in Python, C++, and at least one major deep learning framework (PyTorch, TensorFlow, or JAX).
- Strong understanding of distributed systems, cloud infrastructure (AWS/Azure/GCP), and containerization technologies (Docker/Kubernetes).
- Demonstrated history of deploying production-grade AI models at scale.
- Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.