Job Description
Are you ready to architect the intelligence of tomorrow?
Nexus Horizon Systems is at the forefront of the Generative AI revolution. We are seeking a visionary Senior AI Engineer to join our elite engineering team in San Francisco. You will be responsible for designing, training, and deploying next-generation Large Language Models (LLMs) that power the next wave of enterprise automation.
If you are passionate about pushing the boundaries of artificial intelligence, optimizing model performance at scale, and solving complex data problems, we want to hear from you.
Why Join Us?
- Impact: Work on products used by millions globally.
- Compensation: Competitive salary and equity package.
- Flexibility: Hybrid work model with a focus on autonomy.
Responsibilities
- Design, implement, and fine-tune state-of-the-art Deep Learning models, specifically focusing on Transformers and LLMs.
- Optimize model inference latency and throughput using techniques like quantization, pruning, and distillation.
- Collaborate with cross-functional teams of data scientists, product managers, and engineers to integrate AI models into production pipelines.
- Conduct rigorous A/B testing and performance monitoring to ensure model reliability and accuracy in real-world scenarios.
- Research and prototype novel architectures to improve Natural Language Processing (NLP) capabilities.
- Mentor junior engineers and establish best practices for MLOps within the organization.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related technical field.
- 5+ years of professional experience in AI/ML engineering, with at least 2 years specifically in Generative AI or LLMs.
- Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, or JAX).
- Strong understanding of distributed systems, cloud infrastructure (AWS/GCP/Azure), and containerization (Docker/Kubernetes).
- Experience with vector databases (Pinecone, Milvus) and RAG architectures.
- Exceptional problem-solving skills and the ability to thrive in a fast-paced, ambiguous environment.