Job Description
We are seeking a visionary Senior AI Engineer to join NeuralCore Systems and spearhead the development of next-generation Generative AI models. In this pivotal role, you will architect scalable machine learning pipelines, fine-tune large language models (LLMs), and deploy cutting-edge AI solutions that redefine user experiences. You will work in a high-performance, collaborative environment with a team of world-class data scientists and engineers.
Why Join Us?
- Impactful Work: Build AI that solves real-world problems and shapes the future of technology.
- Top-Tier Compensation: Competitive salary and equity package.
- Modern Stack: Work with the latest in PyTorch, TensorFlow, and cloud-native infrastructure.
- Flexible Culture: Remote-first mindset with a vibrant San Francisco HQ.
If you are passionate about the intersection of deep learning and production engineering, we want to hear from you.
Responsibilities
- Model Development: Design, train, and fine-tune state-of-the-art Large Language Models (LLMs) and generative AI architectures.
- System Architecture: Build scalable, high-performance inference systems capable of handling millions of requests per day.
- Optimization: Implement model quantization, pruning, and distributed training strategies to reduce latency and cost.
- Deployment: MLOps and CI/CD integration for continuous model deployment and monitoring in production environments.
- Research: Stay at the forefront of AI research, experimenting with novel techniques and publishing results.
- Collaboration: Partner with product managers and designers to translate technical capabilities into user-facing features.
Qualifications
- Education: MS or PhD in Computer Science, Mathematics, or a related field with a focus on AI/ML.
- Experience: 5+ years of professional experience in machine learning, deep learning, or natural language processing.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX. Strong experience with Hugging Face Transformers, LangChain, and vector databases.
- Infrastructure: Experience deploying ML models on cloud platforms (AWS, GCP, or Azure) using Docker and Kubernetes.
- Math: Strong understanding of linear algebra, calculus, probability, and statistics.
- Communication: Excellent ability to communicate complex technical concepts to non-technical stakeholders.