Job Description
Are you ready to architect the intelligence of tomorrow?
Nexus Horizon Systems is at the forefront of defining the technological landscape for 2026 and beyond. We are seeking a visionary Lead AI Research Engineer to spearhead the development of next-generation generative AI models and autonomous agent frameworks. If you are passionate about pushing the boundaries of machine learning, ethical AI, and scalable systems, we want to meet you.
In this role, you will not just maintain existing systems; you will build the foundational architecture that powers our predictive analytics and decision-support tools. You will work in a high-performance environment, collaborating with elite data scientists and software architects to deliver solutions that redefine industry standards.
Why join Nexus Horizon?
- Work on cutting-edge technologies that will shape the future.
- Competitive compensation package with equity options.
- Flexible remote-first culture with premium in-office amenities in San Francisco.
Responsibilities
- Architect & Develop: Design and implement scalable machine learning pipelines and proprietary large language models (LLMs) tailored for enterprise applications.
- Research & Innovation: Conduct advanced research in NLP, Computer Vision, and Reinforcement Learning to solve complex, unstructured business problems.
- Model Optimization: Fine-tune and optimize transformer architectures for reduced latency and increased inference accuracy in production environments.
- Collaboration: Partner with product managers and engineering teams to translate research breakthroughs into market-ready features.
- Prototyping: Rapidly prototype new algorithms using Python and modern deep learning frameworks to validate technical feasibility.
- Mentorship: Guide junior researchers and engineers, fostering a culture of continuous learning and technical excellence within the AI division.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Mathematics, Statistics, or a related quantitative field.
- Experience: 5+ years of experience in applied machine learning research or a similar senior engineering role.
- Tech Stack: Proficiency in Python, PyTorch, TensorFlow, or JAX. Experience with Hugging Face Transformers and LangChain.
- Domain Knowledge: Deep understanding of Deep Learning principles, neural networks, and statistical modeling.
- Communication: Exceptional ability to communicate complex technical concepts to non-technical stakeholders and leadership.
- Agility: Proven track record of adapting to rapidly changing tech stacks and research paradigms.