Job Description
Are you ready to define the technology landscape of 2026 and beyond? Nexus Future Labs is seeking a visionary Senior AI Engineer to join our elite team in San Francisco. We are building the foundational systems for the next era of artificial intelligence, and we need a technical leader who can bridge the gap between theoretical research and production-grade scalability.
In this role, you won't just be maintaining legacy systems; you will be architecting the future. You will work directly with our CTO and top-tier researchers to deploy cutting-edge Large Language Models and generative AI solutions that will power our global platform. If you are passionate about pushing the boundaries of what's possible in AI and want to leave a lasting impact on the industry, we want to hear from you.
Why Join Us?
- Shape the Future: Directly influence the roadmap for 2026 product launches.
- Top-Tier Compensation: Competitive salary plus equity package.
- Innovation-First Culture: Work with the brightest minds in tech.
- Flexible Work: Hybrid model supporting your work-life balance.
Responsibilities
- Spearhead the development and deployment of next-generation generative AI models and Large Language Models (LLMs).
- Architect scalable, high-performance machine learning pipelines and data infrastructure.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to define technical requirements.
- Mentor junior engineers and conduct code reviews to maintain high engineering standards.
- Optimize model performance, latency, and resource efficiency for production environments.
- Stay ahead of industry trends in AI research to integrate new methodologies into our stack.
Qualifications
- 5+ years of professional experience in software engineering with a strong focus on Machine Learning or Artificial Intelligence.
- Deep proficiency in Python, PyTorch, TensorFlow, or similar deep learning frameworks.
- Proven experience designing and training Large Language Models or similar NLP architectures.
- Strong understanding of distributed systems, cloud computing (AWS, GCP, or Azure), and containerization (Docker, Kubernetes).
- Experience with MLOps tools and best practices for model versioning and deployment.
- Master’s degree or PhD in Computer Science, Artificial Intelligence, or a related technical field is a plus.