Job Description
We are pioneering the next generation of intelligent systems and are seeking a visionary Senior AI/ML Engineer to join our team in San Francisco. As we look toward the technological landscape of 2026, we need an expert who can architect scalable, high-performance machine learning solutions that redefine user experiences. You will work at the intersection of Generative AI, Large Language Models (LLMs), and autonomous agent systems.
In this role, you will not just implement existing models; you will shape the architecture of the future. You will be responsible for leading complex projects that push the boundaries of what is possible with artificial intelligence.
Why Join Us?
• Future-Ready Role: Work on cutting-edge technologies expected to dominate the industry by 2026.
• Competitive Compensation: Base salary between $180k and $250k, plus equity.
• Flexible Environment: Hybrid work model based in our San Francisco headquarters.
• Impact: Your code will power the next generation of consumer applications.
Responsibilities
- Architect and deploy scalable machine learning pipelines for Large Language Models (LLMs) and Generative AI applications.
- Optimize model inference latency and throughput to support real-time, high-volume user interactions.
- Collaborate with cross-functional teams of data scientists, product managers, and engineers to define AI product requirements.
- Implement robust MLOps strategies to ensure model retraining, monitoring, and deployment in production environments.
- Research and prototype novel algorithms to stay ahead of 2026 AI trends and competitor capabilities.
- Conduct code reviews and mentor junior engineers to foster a culture of technical excellence.
Qualifications
- Master’s or PhD in Computer Science, Statistics, Mathematics, or a related field.
- Minimum of 5+ years of professional experience in machine learning engineering.
- Deep expertise in Python, PyTorch, and TensorFlow frameworks.
- Proven experience fine-tuning large language models and building RAG (Retrieval-Augmented Generation) systems.
- Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).
- Strong understanding of MLOps practices, data versioning, and model monitoring.