Job Description
Are you ready to architect the intelligent systems that will define the year 2026 and beyond? Apex Future Systems is seeking a visionary Lead AI Architect to spearhead our next-generation research and development initiatives. In this pivotal role, you will bridge the gap between theoretical AI breakthroughs and scalable production solutions, shaping the future of autonomous agents and generative AI.
Join a team of industry pioneers in the heart of San Francisco. We offer a competitive salary, comprehensive benefits, and the opportunity to work on projects that will impact millions of users globally.
Why Join Us?
- Work on cutting-edge technologies targeting the 2026 market landscape.
- Competitive compensation package ($160k - $240k).
- Flexible hybrid work model in San Francisco.
- Equity opportunities in a high-growth startup.
Responsibilities
- Architect and oversee the development of large-scale machine learning pipelines focused on the 2026 technology roadmap.
- Lead a high-performing team of data scientists and ML engineers to deliver high-impact AI products.
- Define best practices for model deployment, monitoring, and MLOps within the organization.
- Conduct research into emerging AI paradigms, including reinforcement learning and multi-modal models.
- Collaborate with cross-functional stakeholders to translate business requirements into technical AI solutions.
- Ensure system scalability, security, and ethical AI compliance across all projects.
Qualifications
- Master’s or Ph.D. in Computer Science, Artificial Intelligence, or a related quantitative field (or equivalent extensive industry experience).
- Minimum of 5+ years of experience in designing and deploying production-level AI systems.
- Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Deep understanding of distributed systems, cloud architecture (AWS/Azure/GCP), and containerization (Docker/Kubernetes).
- Proven track record of leading technical teams and mentoring junior developers.
- Experience with NLP, Computer Vision, or Large Language Models is highly preferred.