Job Description
Are you ready to architect the intelligence of tomorrow?
Nexus Future Labs is seeking a visionary Senior AI/ML Engineer to join our elite engineering team in San Francisco. We are not just building software; we are defining the trajectory of artificial intelligence for the next decade. If you are passionate about pushing the boundaries of Large Language Models (LLMs), generative AI, and scalable machine learning infrastructure, we want to hear from you.
In this role, you will spearhead the development of cutting-edge AI solutions that drive real-world impact, working alongside world-class researchers and engineers. You will have the autonomy to design systems from the ground up and the resources to experiment with the latest in neural architecture search and automated machine learning.
Responsibilities
- Design & Deployment: Architect and deploy robust, scalable machine learning models and AI services using modern cloud infrastructure (AWS/GCP).
- Model Optimization: Fine-tune and optimize state-of-the-art foundation models for specific domain applications, focusing on inference speed and cost-efficiency.
- Data Strategy: Lead the end-to-end data lifecycle, from data ingestion and cleaning to feature engineering and model evaluation.
- Collaboration: Partner closely with product managers and data scientists to translate complex business requirements into technical AI solutions.
- Research: Stay at the forefront of AI research, implementing novel techniques (e.g., RAG, Transformers, Reinforcement Learning) into production environments.
- Mentorship: Mentor junior engineers and contribute to a culture of technical excellence and continuous learning within the team.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, or a related technical field (PhD preferred).
- Experience: 5+ years of professional experience in machine learning engineering, data science, or a similar role.
- Technical Stack: Deep proficiency in Python, PyTorch, TensorFlow, or JAX. Experience with MLOps tools (MLflow, Kubeflow) and containerization (Docker, Kubernetes).
- Cloud Expertise: Proven experience deploying models on major cloud providers (AWS, GCP, or Azure).
- Problem Solving: Strong analytical skills with a track record of solving complex technical challenges in large-scale systems.
- Communication: Excellent verbal and written communication skills, capable of explaining complex technical concepts to non-technical stakeholders.