Job Description
Nexus Future Labs is at the forefront of the artificial intelligence revolution, building next-generation generative models that redefine human-machine interaction. We are seeking a Senior AI Engineer to join our elite technical team and drive the innovation of our flagship products.
In this role, you will bridge the gap between cutting-edge research and scalable production engineering. You will own the full lifecycle of AI model development, from experimentation and training to deployment and monitoring. If you are passionate about pushing the boundaries of what is possible with Large Language Models (LLMs) and have a track record of delivering high-impact technical solutions, we want to hear from you.
Why Join Us?
- Competitive Compensation: $160k - $240k base salary plus equity.
- Flexible Work: Hybrid model based in San Francisco with remote flexibility.
- Growth: Work with industry leaders in AI, NLP, and Cloud Architecture.
Responsibilities
- Design, train, and fine-tune large-scale deep learning models, specifically focusing on LLMs and transformer architectures.
- Build and optimize high-throughput, low-latency inference pipelines serving millions of requests daily.
- Collaborate closely with Data Scientists and Product Managers to translate business requirements into technical AI solutions.
- Implement robust MLOps strategies to ensure model reliability, monitoring, and continuous retraining.
- Conduct rigorous code reviews and contribute to the engineering culture of excellence.
- Stay abreast of the latest research in AI and evaluate new techniques for integration into our product stack.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related technical field (4+ years of relevant experience).
- Extensive experience with Python, PyTorch, TensorFlow, or JAX.
- Deep understanding of machine learning fundamentals, including neural networks, optimization, and statistical methods.
- Strong proficiency in cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).
- Experience with vector databases and RAG (Retrieval-Augmented Generation) architectures.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.