Job Description
We are seeking a visionary Lead AI Architect to spearhead Project 2026, our revolutionary generative AI platform. This role is pivotal in defining the technical roadmap for next-generation autonomous systems designed to solve complex global challenges.
As a key member of our elite engineering team, you will bridge the gap between theoretical research and production-grade systems. You will work in a fast-paced, high-impact environment where your code will shape the future of enterprise intelligence.
Why join Project 2026?
- Work on cutting-edge Large Language Models and multimodal systems.
- Competitive compensation and equity packages.
- Flexible remote-first culture with a vibrant San Francisco hub.
Responsibilities
- Architectural Design: Design and implement scalable, fault-tolerant AI systems and microservices infrastructure for Project 2026.
- Research Integration: Translate cutting-edge research papers into production-ready algorithms and code.
- Technical Leadership: Mentor a team of senior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Performance Optimization: Drive initiatives to optimize model inference latency and reduce computational costs.
- Cross-Functional Collaboration: Partner with product managers, security teams, and stakeholders to define technical requirements and roadmaps.
- Best Practices: Establish and enforce coding standards, CI/CD pipelines, and cloud governance policies.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related technical field.
- Experience: 8+ years of experience in software engineering, with at least 5 years specifically focused on AI/ML architecture.
- Technical Skills: Proficiency in Python, TensorFlow, PyTorch, or JAX. Deep understanding of distributed systems (Kubernetes, AWS, GCP).
- Generative AI: Proven experience working with LLMs, fine-tuning models, and RAG (Retrieval-Augmented Generation) architectures.
- Communication: Exceptional ability to communicate complex technical concepts to non-technical stakeholders.