Job Description
We are on the cusp of a technological renaissance. At Nexus Horizon, we are building the intelligent infrastructure for the future, targeting breakthroughs that will define the landscape of 2026 and beyond. We are seeking a visionary Senior AI/ML Architect to lead our research and engineering teams in developing next-generation Large Language Models (LLMs) and autonomous agents.
In this role, you will not just write code; you will define the architectural standards for the next era of artificial intelligence. You will work directly with C-level executives to translate complex business challenges into elegant, scalable AI solutions.
Why join us?
- Work on cutting-edge Generative AI projects.
- Competitive equity and stock options in a high-growth startup.
- Flexible remote-first culture with a hub in San Francisco.
Responsibilities
- Architectural Leadership: Design and implement scalable, distributed machine learning systems capable of handling petabyte-scale data ingestion.
- Model Development: Spearhead the research and deployment of proprietary LLMs, focusing on efficiency, hallucination reduction, and domain adaptation.
- System Optimization: Oversee the full machine learning lifecycle (MLOps), ensuring models are deployed in production environments with real-time inference capabilities.
- Team Mentorship: Mentor junior engineers and data scientists, fostering a culture of continuous learning and innovation.
- Strategic Roadmapping: Collaborate with product teams to identify high-impact AI use cases that drive revenue and user engagement.
Qualifications
- Education: MS or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.
- Experience: 7+ years of experience in software engineering, with at least 4 years focused on Machine Learning and Deep Learning.
- Technical Skills: Deep expertise in Python, PyTorch, TensorFlow, and modern MLOps tools (Kubeflow, MLflow, Docker, Kubernetes).
- AI Expertise: Proven track record of training and fine-tuning large-scale transformer models.
- Problem Solving: Strong ability to troubleshoot complex architectural challenges and optimize model latency and throughput.