Job Description
Are you ready to architect the future of intelligent systems? Nexus Horizon Systems is pioneering the next generation of Artificial Intelligence. We are looking for a visionary AI/ML Architect to lead our 2026 roadmap initiatives. In this role, you will not just build models; you will define the architectural framework for scalable, ethical, and transformative AI solutions that will dominate the global market.
Join a team of elite engineers and data scientists pushing the boundaries of what is possible in machine learning, generative AI, and predictive analytics.
Why Join Us?
- Impact: Work on core infrastructure that powers next-gen consumer products.
- Growth: Unlimited learning budget and access to cutting-edge hardware.
- Flexibility: Hybrid work model based in the heart of San Francisco.
Responsibilities
- Design & Strategy: Define the high-level architecture for large-scale machine learning systems, ensuring scalability, security, and performance.
- Model Development: Lead the research and development of proprietary AI models, including NLP, Computer Vision, and Reinforcement Learning.
- Team Leadership: Mentor a team of data scientists and ML engineers, fostering a culture of innovation and technical excellence.
- Deployment: Oversee the MLOps pipeline, ensuring seamless model training, validation, and deployment to production environments.
- Technology Evaluation: Stay ahead of the curve by evaluating emerging technologies and frameworks (e.g., LLMs, Edge AI) to drive product innovation.
- Collaboration: Partner with product managers and engineering teams to translate business requirements into technical AI solutions.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Mathematics, or a related field.
- Experience: 7+ years of experience in software engineering and machine learning, with at least 3 years in a senior architectural or lead role.
- Technical Skills: Proficiency in Python, TensorFlow, PyTorch, or JAX. Strong experience with distributed computing frameworks (Spark, Kubernetes).
- Cloud Expertise: Deep understanding of cloud platforms (AWS, GCP, or Azure) and serverless architectures.
- Mathematical Foundation: Strong grasp of linear algebra, calculus, and probability theory.
- Communication: Excellent verbal and written communication skills; ability to explain complex technical concepts to non-technical stakeholders.