Job Description
Are you ready to architect the intelligence of tomorrow? Nexus Future Labs is pioneering the next generation of Generative AI systems, and we are looking for a visionary Senior AI/ML Engineer to join our elite team in San Francisco.
As we prepare for the 2026 technological landscape, we need a technical leader who thrives on ambiguity and possesses the expertise to build scalable, safe, and transformative AI models. This is not just a job; it is an opportunity to shape the future of human-machine interaction.
Why Join Us?
- Next-Gen Technology: Work on cutting-edge Large Language Models (LLMs) and autonomous agents.
- Premier Location: Collaborate with the brightest minds in the heart of Silicon Valley.
- Impactful Work: Your code will power the AI infrastructure for industries worldwide.
Responsibilities
- Design, train, and deploy state-of-the-art Generative AI models (LLMs, Diffusion Models) for high-volume production environments.
- Architect scalable MLOps pipelines to ensure model reliability, latency optimization, and continuous integration.
- Lead research initiatives into emerging AI paradigms, including Reinforcement Learning from Human Feedback (RLHF) and Chain-of-Thought reasoning.
- Collaborate cross-functionally with product managers and engineering teams to translate complex AI capabilities into user-centric products.
- Establish best practices for ethical AI, bias mitigation, and data privacy compliance.
- Conduct rigorous code reviews and mentor junior engineers to foster a culture of technical excellence.
- Optimize inference costs and GPU utilization for real-time applications.
Qualifications
- Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- 5+ years of professional experience in software engineering with a strong focus on Machine Learning.
- Proficiency in Python, PyTorch, TensorFlow, and modern deep learning frameworks.
- Deep understanding of NLP architectures, transformers, and fine-tuning techniques.
- Experience with cloud platforms (AWS/GCP/Azure) and containerization technologies (Docker, Kubernetes).
- Strong background in statistics, linear algebra, and optimization theory.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.