Job Description
We are seeking a visionary Senior Machine Learning Engineer to join our elite AI R&D team in San Francisco. NeuralHorizon AI is pioneering the next generation of Large Language Models (LLMs) and generative agents. You will be responsible for building the infrastructure that scales our models from research prototypes to production-grade systems serving millions of users. If you are passionate about pushing the boundaries of what is possible with AI and want to work in a fast-paced, high-impact environment, we want to hear from you.
Why Join Us?
- Work with state-of-the-art Generative AI and LLM technologies.
- Competitive equity package and top-tier healthcare.
- Flexible remote-first culture with a vibrant SF office hub.
- Continuous learning budget and access to the latest hardware.
Responsibilities
- Model Development & Training: Design, implement, and optimize deep learning models, specifically focusing on Transformer architectures and NLP tasks.
- Production Pipelines: Build scalable MLOps pipelines to manage the full lifecycle of models, from data ingestion to deployment and monitoring.
- Performance Optimization: Reduce inference latency and improve model throughput using techniques such as quantization, pruning, and model distillation.
- Research Integration: Collaborate closely with research scientists to translate cutting-edge academic papers into production-ready software.
- Code Review & Mentorship: Mentor junior engineers and conduct rigorous code reviews to ensure code quality and architectural best practices.
Qualifications
- Education: MS or PhD in Computer Science, Mathematics, Statistics, or a related field with a focus on AI/ML.
- Experience: 5+ years of professional experience in Machine Learning or Deep Learning engineering roles.
- Programming: Strong proficiency in Python, PyTorch, or TensorFlow.
- Domain Knowledge: Deep understanding of Natural Language Processing (NLP), Large Language Models (LLMs), and prompt engineering strategies.
- Infrastructure: Experience with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).
- Problem Solving: Proven track record of solving complex engineering problems in high-scale environments.