Job Description
We are at the precipice of a technological renaissance. OmniStream Systems is seeking a visionary Future AI Architect to lead our R&D division in defining the infrastructure of the year 2026 and beyond. In this pivotal role, you won't just write code; you will architect the cognitive frameworks that will power the next generation of human-machine symbiosis. If you are driven by the possibility of shaping the future and possess an unparalleled command of emerging AI paradigms, we want to hear from you.
Why Join Us?
- Work with the brightest minds in Silicon Valley on projects that define the future of technology.
- Competitive compensation package including equity options and performance bonuses.
- Flexible remote-first policy with access to state-of-the-art facilities in San Francisco.
- Focus on long-term research with immediate tangible impact on our product roadmap.
As we look toward 2026, our mission is to integrate artificial general intelligence into core enterprise solutions seamlessly. You will be the bridge between theoretical research and scalable production systems.
Responsibilities
- Architect Next-Gen Systems: Design and implement scalable neural architectures capable of handling complex, adaptive learning tasks for the 2026 era.
- Model Optimization: Fine-tune large language models and generative AI agents to ensure high-efficiency inference and reduced computational overhead.
- R&D Leadership: Lead a cross-functional team of data scientists and engineers to explore novel AI methodologies, including Neuromorphic computing and quantum-inspired algorithms.
- Ethical AI Compliance: Establish rigorous safety protocols and ethical guidelines to ensure AI deployment is transparent, unbiased, and responsible.
- Prototype Development: Build and iterate on Proof-of-Concept (PoC) systems that demonstrate the viability of future AI technologies in real-world scenarios.
- Strategic Vision: Collaborate with C-suite executives to align AI capabilities with long-term business objectives and market trends.
Qualifications
- Advanced Education: PhD or Master’s degree in Computer Science, Artificial Intelligence, or a related quantitative field.
- Technical Proficiency: Deep expertise in Python, PyTorch, TensorFlow, and experience with distributed computing frameworks (e.g., Kubernetes, Ray).
- Experience: 5+ years of professional experience in machine learning engineering, specifically with LLMs and reinforcement learning.
- Innovation Mindset: Demonstrated history of publishing research papers or creating proprietary technologies that push the boundaries of current AI capabilities.
- System Design: Strong ability to design resilient, fault-tolerant systems that scale from prototype to production.
- Communication: Exceptional ability to articulate complex technical concepts to non-technical stakeholders and leadership.