Job Description
Shape the Future of Intelligence at Nexus Future Labs
We are on the cusp of a new era in technology, and we are looking for a visionary Senior Generative AI Engineer to lead our initiatives. As we move toward 2026, the definition of artificial intelligence is evolving from simple automation to complex, autonomous reasoning and creation. You will be at the helm of designing, training, and deploying state-of-the-art Large Language Models (LLMs) that will redefine how humans interact with software.
In this role, you won't just be writing code; you will architect the brain of our next-generation products. If you are passionate about the ethical implications of AI, possess deep technical expertise in machine learning architectures, and want to work in the heart of the tech capital, we want to meet you.
Why Join Us?
- Work with cutting-edge hardware and proprietary datasets.
- Competitive equity package and comprehensive benefits.
- Flexible remote-first policy with a hub in San Francisco.
Responsibilities
- Design and implement scalable Generative AI architectures, focusing on LLMs and diffusion models.
- Optimize model inference pipelines to reduce latency and increase throughput for real-time applications.
- Research and fine-tune foundation models on domain-specific datasets to enhance performance and reduce hallucinations.
- Collaborate with cross-functional teams of engineers, product managers, and ethicists to ensure AI safety and compliance.
- Mentor junior engineers and establish coding standards for AI projects.
- Stay abreast of the latest advancements in AI research (NeurIPS, ICML, etc.) and integrate breakthroughs into our product roadmap.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related technical field with a focus on AI/ML.
- 5+ years of professional experience in machine learning, deep learning, or natural language processing.
- Strong proficiency in Python, PyTorch, or TensorFlow.
- Proven experience in training and fine-tuning large-scale transformer models.
- Deep understanding of MLOps, CI/CD for ML, and cloud infrastructure (AWS/GCP/Azure).
- Experience with vector databases and Retrieval-Augmented Generation (RAG) architectures.