Job Description
Quantum Dynamics is pioneering the future of generative AI and machine learning infrastructure. We are seeking a visionary Senior AI Engineer to join our elite engineering team in San Francisco. In this role, you will be at the forefront of developing next-generation Large Language Models (LLMs) and scalable deep learning systems that power our enterprise clients.
As a Senior AI Engineer, you will bridge the gap between theoretical research and production-grade deployment. You will work in a collaborative environment with world-class researchers and engineers to optimize model performance, reduce latency, and ensure robustness in high-stakes applications.
Why join Quantum Dynamics?
- Work on cutting-edge AI technology that impacts millions of users.
- Competitive compensation package including equity.
- Flexible remote-first policy with a vibrant SF office culture.
- Access to state-of-the-art hardware and cloud resources.
Responsibilities
- Design, train, and fine-tune state-of-the-art deep learning models, including Transformers and diffusion models, for specific domain applications.
- Optimize model inference performance and resource utilization, reducing latency and cost per token.
- Build and maintain robust data pipelines for training, validation, and evaluation datasets.
- Collaborate with product managers and engineers to integrate AI models into production applications via APIs and microservices.
- Conduct rigorous A/B testing and model monitoring to ensure accuracy, safety, and reliability over time.
- Research and implement novel techniques in Natural Language Processing (NLP) and Computer Vision.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related field (4+ years of industry experience may substitute for advanced degree).
- Strong proficiency in Python and C++.
- Expert knowledge of deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Proven experience with MLOps tools (MLflow, Kubeflow, Docker, Kubernetes) for model deployment and monitoring.
- Deep understanding of LLM architectures (e.g., BERT, GPT, Llama) and fine-tuning methodologies (PEFT, LoRA).
- Experience with vector databases (Pinecone, Milvus) and RAG architectures.
- Excellent communication skills and the ability to mentor junior engineers.