Job Description
We are currently assembling the visionary team for Project 2026, our next-generation initiative to revolutionize autonomous decision-making systems. As the Lead AI Architect, you will be at the forefront of defining the architectural blueprints for our proprietary neural processing units and next-generation Large Language Models (LLMs).
In this role, you won't just maintain existing systems; you will pioneer the infrastructure that scales to billions of concurrent requests. We are looking for a technical heavyweight who thrives in ambiguity and is obsessed with pushing the boundaries of what is possible in artificial intelligence.
Why Join Us?
- Work on cutting-edge AI infrastructure that sets the industry standard for 2026.
- Competitive equity package and top-tier compensation.
- Flexible remote-first culture with quarterly innovation sprints in San Francisco.
Responsibilities
- Design and implement scalable, fault-tolerant AI architectures for Project 2026.
- Lead the migration and optimization of legacy ML pipelines to modern distributed computing environments (Kubernetes, Ray).
- Define best practices for MLOps, including model versioning, A/B testing, and continuous integration/continuous deployment (CI/CD).
- Collaborate with cross-functional teams of researchers, engineers, and product managers to translate business requirements into technical roadmaps.
- Ensure ethical AI compliance and fairness in algorithmic outputs.
- Conduct technical deep-dives to identify performance bottlenecks in large-scale neural networks.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related field, or equivalent extensive industry experience.
- 7+ years of professional experience in software engineering, with at least 3 years focused on Machine Learning and AI.
- Expert proficiency in Python, C++, and modern deep learning frameworks (PyTorch or TensorFlow).
- Deep understanding of distributed systems, cloud architecture (AWS/GCP/Azure), and high-performance computing.
- Proven track record of deploying production-grade AI models at scale.
- Strong experience with vector databases and semantic search technologies.