Job Description
Are you ready to architect the future? 2026 is seeking a visionary Senior AI Engineer to join our elite team in San Francisco. We are building the foundational intelligence for the next generation of autonomous systems.
At 2026, we don't just predict the future; we engineer it. Our mission is to revolutionize how machines understand and interact with the world using advanced Large Language Models and Generative AI. As a Senior AI Engineer, you will be at the forefront of innovation, pushing the boundaries of what is possible in neural architecture and scalable machine learning infrastructure.
Why Join 2026?
- Work on cutting-edge AI research with direct impact on global industries.
- Competitive compensation package and equity opportunities.
- Flexible remote-first culture with a central hub in San Francisco.
- Access to state-of-the-art hardware and research resources.
If you are passionate about Deep Learning, NLP, and building scalable AI systems, we want to hear from you.
Responsibilities
- Model Development: Design, train, and optimize large-scale deep learning models for natural language processing and generative tasks.
- Infrastructure Scaling: Architect robust, high-throughput pipelines for model training and inference using cloud-native technologies.
- Research & Innovation: Stay at the bleeding edge of AI research, implementing novel architectures and techniques to improve model performance.
- Collaboration: Partner with product managers and engineers to translate complex AI capabilities into user-centric applications.
- Mentorship: Guide junior engineers and researchers, fostering a culture of technical excellence and continuous learning.
- Optimization: Fine-tune models for speed, accuracy, and resource efficiency to ensure deployment success.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.
- Experience: 5+ years of professional experience in machine learning engineering or applied AI research.
- Technical Skills: Strong proficiency in Python, PyTorch, or TensorFlow. Deep understanding of Transformer architectures and LLMs.
- System Design: Experience designing distributed systems and MLOps pipelines (e.g., Docker, Kubernetes, AWS/GCP).
- Problem Solving: Proven track record of solving complex, ambiguous problems in high-stakes environments.
- Communication: Excellent ability to communicate technical concepts to both technical and non-technical stakeholders.