Job Description
Join the Architects of 2026
Nexus Horizon AI is at the forefront of the next industrial revolution. We are building the autonomous systems and generative intelligence frameworks that will define the year 2026 and beyond. As a Senior AI/ML Engineer, you won't just be maintaining models; you will be architecting the future of human-machine collaboration.
We are seeking a visionary technical leader to join our San Francisco headquarters. You will work with a world-class team of researchers and engineers to build Large Action Models (LAMs) and scalable generative AI agents. If you are passionate about pushing the boundaries of what is possible in artificial intelligence, this is your opportunity to shape the future.
Why Join Us?
- Work on cutting-edge AI infrastructure that powers the next generation of software.
- Competitive equity package and top-tier compensation.
- Flexible remote-first culture with a collaborative HQ in the heart of SF.
Responsibilities
- Architect Next-Gen Models: Design and implement state-of-the-art Machine Learning models and neural architectures, focusing on scalability and efficiency for 2026 infrastructure.
- Optimize Inference Pipelines: Develop high-performance inference systems to reduce latency and cost, ensuring real-time AI responsiveness.
- Lead Research & Development: Conduct experimental research in Generative AI, specifically exploring Retrieval-Augmented Generation (RAG) and Agentic workflows.
- Collaborate on Data Strategy: Partner with data engineers to build robust, high-quality datasets that fuel our AI models.
- Ensure AI Safety: Implement rigorous testing and validation protocols to ensure model fairness, bias mitigation, and robustness.
- Mentor Junior Engineers: Guide a team of talented engineers, fostering a culture of continuous learning and technical excellence.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field.
- Experience: 5+ years of professional experience in building and deploying production-level Machine Learning models.
- Technical Stack: Deep expertise in Python, PyTorch, or TensorFlow. Experience with LangChain, Hugging Face, and Vector Databases (Pinecone, Milvus).
- Domain Knowledge: Strong understanding of Natural Language Processing (NLP), Large Language Models (LLMs), and Deep Learning fundamentals.
- Problem Solving: Demonstrated ability to tackle complex, unstructured problems and derive data-driven solutions.
- Communication: Excellent ability to communicate complex technical concepts to non-technical stakeholders and cross-functional teams.