Job Description
Are you ready to architect the next generation of artificial intelligence? Neural Horizon Systems is seeking a visionary Senior AI/ML Engineer to lead the development of our proprietary Large Language Models (LLMs) and neural network architectures. We are at the forefront of the AI revolution, building tools that redefine human-computer interaction.
In this role, you will not just use existing tools; you will push the boundaries of what is possible, optimizing for performance, scalability, and ethical AI deployment. If you are passionate about Deep Learning, NLP, and building systems that learn, we want to meet you.
Why Join Us?
- Impactful Work: Deploy models that directly influence millions of users.
- Top-Tier Tech Stack: Work with cutting-edge frameworks like PyTorch and JAX.
- Competitive Compensation: Comprehensive benefits package including equity.
- Flexible Environment: Hybrid work model based in the heart of San Francisco.
Ready to shape the future? Apply today.
Responsibilities
- Model Architecture: Design, train, and fine-tune state-of-the-art deep learning models, including Transformers and GNNs, tailored for specific business use cases.
- Production Deployment: Engineer scalable ML pipelines using MLOps best practices (CI/CD, Docker, Kubernetes) to move models from research to production efficiently.
- Performance Optimization: Optimize model inference latency and reduce computational costs while maintaining high accuracy.
- Research & Innovation: Stay abreast of the latest academic papers and industry trends, implementing novel techniques such as LoRA or Quantization.
- Collaboration: Partner closely with data scientists, software engineers, and product managers to define technical requirements and solve complex problems.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Mathematics, Statistics, or a related field (or equivalent practical experience).
- Experience: 5+ years of professional experience in Machine Learning Engineering or Data Science.
- Programming: Expert proficiency in Python and experience with ML frameworks (PyTorch, TensorFlow, or JAX).
- Cloud Infrastructure: Strong experience deploying models on cloud platforms (AWS, GCP, or Azure).
- Tools: Familiarity with MLOps tools (MLflow, Kubeflow) and vector databases (Pinecone, Weaviate, Milvus).
- Communication: Excellent ability to communicate complex technical concepts to non-technical stakeholders.