Job Description
We are on a mission to redefine the boundaries of artificial intelligence. Nebula AI Solutions is seeking a visionary Senior Machine Learning Engineer to join our elite team in San Francisco. If you are passionate about building scalable, transformative AI systems that impact millions of users, we want to hear from you.
In this role, you will work at the intersection of research and engineering, deploying state-of-the-art models in production environments. You will collaborate with world-class data scientists and engineers to push the envelope of Natural Language Processing (NLP) and Computer Vision.
Why Join Us?
- Competitive salary and equity package.
- Flexible remote-first culture with a SF office hub.
- Access to cutting-edge hardware and cloud infrastructure.
- Continuous learning opportunities in the latest AI research.
Responsibilities
- Model Development: Design, train, and fine-tune complex deep learning models, specifically focusing on Large Language Models (LLMs) and generative AI architectures.
- Production Deployment: Implement MLOps pipelines to deploy models into production, ensuring high availability, scalability, and low latency.
- Data Engineering: Collaborate with data engineers to build robust data pipelines, ensuring data quality, privacy, and integrity for training purposes.
- Research & Innovation: Stay abreast of the latest academic papers and industry trends, translating theoretical research into practical engineering solutions.
- Code Review & Mentorship: Lead code reviews and mentor junior engineers and data scientists, fostering a culture of technical excellence.
- System Optimization: Continuously optimize model inference speeds and reduce resource consumption to improve cost-efficiency.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, Mathematics, or a related technical field.
- Programming: Proficiency in Python with deep expertise in libraries such as PyTorch, TensorFlow, or JAX.
- Experience: Minimum of 5+ years of experience in machine learning engineering or applied research.
- Frameworks: Strong hands-on experience with ML frameworks, cloud platforms (AWS/GCP/Azure), and containerization tools (Docker, Kubernetes).
- NLP: Proven track record of working with NLP tasks, including text classification, sentiment analysis, or sequence-to-sequence modeling.
- Communication: Excellent verbal and written communication skills, capable of explaining complex technical concepts to diverse stakeholders.