Job Description
Are you ready to architect the future? 2026 is a pioneer in next-generation artificial intelligence, dedicated to building scalable, ethical, and transformative AI solutions for the enterprise market. We are looking for a visionary Senior AI/ML Engineer to join our elite engineering team in San Francisco.
In this role, you won't just maintain existing models; you will design and deploy the core infrastructure that powers our proprietary LLMs and predictive analytics engines. You will work at the intersection of research and production, optimizing models for high-throughput inference while ensuring robust security and compliance standards.
If you are passionate about pushing the boundaries of what's possible with machine learning and want to leave a lasting impact on the industry, we want to hear from you.
Responsibilities
- Design, train, and deploy advanced machine learning models and deep learning architectures, specifically focusing on Generative AI and NLP.
- Optimize existing models for speed, latency, and cost-efficiency, reducing inference costs by up to 40%.
- Collaborate closely with data scientists and product managers to translate complex business requirements into technical AI solutions.
- Build and maintain robust MLOps pipelines using tools like Kubernetes, Docker, and AWS SageMaker.
- Conduct rigorous A/B testing and model evaluation to ensure production-grade accuracy and reliability.
- Mentor junior engineers and provide technical leadership within the AI research group.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related quantitative field.
- Minimum of 5+ years of professional experience in machine learning engineering, with at least 2 years in a senior role.
- Deep proficiency in Python, PyTorch, TensorFlow, or JAX.
- Extensive experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies.
- Strong understanding of distributed systems, data structures, and algorithm design.
- Proven track record of deploying large-scale models into production environments.