Job Description
Are you ready to define the technological landscape of the future? Nexus Future Labs is seeking a visionary Senior AI/ML Architect to lead the development of next-generation systems designed for the 2026 era. In this pivotal role, you won't just build models; you will architect the cognitive infrastructure of tomorrow. We are looking for pioneers who are obsessed with the convergence of Agentic AI, Quantum Computing, and Sustainable Machine Learning. If you thrive in ambiguity and want to solve the world's most complex challenges before they arise, we want to hear from you.
Why Join Us?
- Work at the forefront of AI innovation, preparing systems for the 2026 market landscape.
- Competitive compensation package with equity options.
- Flexible remote-first culture with a premium office in San Francisco.
- Access to state-of-the-art hardware and quantum simulation labs.
Responsibilities
- Architectural Leadership: Design and deploy scalable machine learning pipelines capable of processing petabytes of data in real-time with sub-millisecond latency.
- Agentic AI Development: Lead the research and implementation of autonomous, multi-agent AI workflows that can self-correct and optimize complex decision-making processes.
- Quantum Integration: Integrate quantum-ready algorithms into existing cloud infrastructure to enhance computational speed for optimization problems.
- AI Governance: Establish rigorous best practices for AI governance, ensuring transparency, fairness, and robust security standards for all deployed models.
- Mentorship: Mentor a team of talented data scientists and engineers, fostering a culture of continuous learning and technical excellence.
- Strategic Roadmapping: Collaborate with cross-functional product teams to translate 2026 strategic vision into actionable technical roadmaps.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
- Experience: Proven experience (7+ years) in building production-grade ML systems, specifically in Natural Language Processing (NLP) or Reinforcement Learning.
- Technical Stack: Deep expertise in Python, PyTorch, TensorFlow, and distributed computing frameworks (Kubernetes, Apache Spark, Ray).
- Cloud Mastery: Extensive experience with cloud platforms (AWS, GCP, Azure) and MLOps tooling (MLflow, Kubeflow).
- Future-Proofing: Strong understanding of emerging trends in quantum computing and their application to AI optimization.
- Communication: Excellent communication skills with the ability to translate complex technical concepts for diverse stakeholders.