Job Description
We are looking for a visionary Senior Generative AI Engineer to lead the charge in developing the AI infrastructure of tomorrow. As we look toward the technological landscape of 2026, we are building systems that don't just predict the future but actively create it.
In this role, you will design and implement large-scale language models (LLMs) and multimodal systems, pushing the boundaries of what's possible with Natural Language Processing (NLP) and Generative AI. You will work in a high-impact environment where innovation is the currency, and your code will shape the next generation of intelligent applications.
Why Join Us?
- Work on cutting-edge LLMs and Foundation Models.
- Competitive equity and top-tier compensation package.
- Flexible remote-first culture with a hub in the heart of San Francisco.
- Access to the latest hardware for high-performance computing.
Responsibilities
- Architecture & Development: Design, train, and fine-tune large-scale generative models (GPT-4, Llama 3, etc.) using PyTorch and TensorFlow.
- Optimization: Implement model quantization, distillation, and optimization techniques to ensure low-latency, high-throughput inference.
- RAG Systems: Build robust Retrieval-Augmented Generation pipelines to minimize hallucinations and improve factual accuracy.
- Collaboration: Partner with Product Managers and Data Scientists to translate complex business requirements into scalable AI solutions.
- MLOps: Establish CI/CD pipelines and monitoring systems to ensure production-grade reliability and model drift detection.
- Ethics & Compliance: Enforce rigorous safety guidelines, bias mitigation strategies, and data privacy standards (GDPR/CCPA).
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of experience in software engineering with a specialized focus on AI/ML.
- Technical Skills: Deep understanding of Transformer architectures, BERT, and Generative Adversarial Networks (GANs).
- Programming: Proficiency in Python, C++, and experience with cloud platforms (AWS, GCP, or Azure).
- Infrastructure: Hands-on experience with vector databases (Pinecone, Milvus, Weaviate) and MLOps tools (Kubeflow, MLflow).
- Soft Skills: Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders.