Job Description
Are you ready to architect the neural networks that will define the next generation of human-computer interaction? We are seeking a Senior AI Engineer to join our elite team in San Francisco. In this pivotal role, you will spearhead the development of next-generation Large Language Models (LLMs) and generative AI solutions that drive our core product innovation. If you are passionate about pushing the boundaries of artificial intelligence and want to work in a high-performance, forward-thinking environment, this is your opportunity to shape the future.
Why Join Us?
- Work on cutting-edge AI technology that impacts millions of users.
- Competitive compensation package with equity options.
- Flexible remote-first culture with a hub in the heart of the Bay Area.
- Access to top-tier compute resources and the latest hardware.
The Role:
As a Senior AI Engineer, you will bridge the gap between theoretical research and production-grade deployment. You will collaborate with a multidisciplinary team of researchers, data scientists, and product engineers to build scalable AI systems.
Responsibilities
- Design, train, and fine-tune large-scale machine learning models, specifically focusing on NLP and LLMs.
- Optimize model inference performance and reduce latency in real-time applications.
- Collaborate with the MLOps team to implement robust CI/CD pipelines for model deployment.
- Conduct rigorous A/B testing and analysis to validate model performance against business metrics.
- Stay abreast of the latest advancements in the AI field and evaluate their potential application to our products.
- Mentor junior engineers and provide technical guidance on best practices in AI architecture.
Qualifications
- Master’s degree or Ph.D. in Computer Science, Mathematics, or a related quantitative field.
- 5+ years of professional experience in AI/ML engineering or research.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Strong experience with LLMs, transformers, and prompt engineering.
- Familiarity with cloud platforms (AWS, GCP) and containerization technologies (Docker, Kubernetes).
- Proven track record of deploying models to production environments.
- Excellent communication skills and ability to translate complex technical concepts for non-technical stakeholders.