Job Description
Are you ready to shape the future of intelligence? Nexus Future Labs is seeking a visionary Senior AI Engineer to join our elite engineering team. We are at the forefront of developing next-generation Generative AI models that are revolutionizing industries from healthcare to finance. If you are passionate about building scalable, robust, and ethical AI systems, we want to hear from you.
In this role, you will work with a world-class team of data scientists and researchers to architect and deploy state-of-the-art Machine Learning (ML) solutions. You will be responsible for the entire ML lifecycle, from model design and training to production deployment and optimization. Join us in building the technological foundation for tomorrow's innovations.
Why Join Nexus Future Labs?
- Impactful Work: Build AI solutions that solve real-world problems.
- Top-Tier Team: Collaborate with industry leaders and innovators.
- Competitive Package: $160k - $220k base salary + equity + comprehensive benefits.
- Flexible Environment: Hybrid work model based in the heart of San Francisco.
Responsibilities
- Design, develop, and deploy scalable Machine Learning models and AI applications using Python and modern deep learning frameworks.
- Optimize existing models for inference speed, accuracy, and resource efficiency using techniques like quantization and pruning.
- Collaborate with cross-functional teams (Data Engineering, Product, Design) to translate business requirements into technical solutions.
- Research and implement cutting-edge algorithms in Natural Language Processing (NLP) and Computer Vision.
- Maintain and monitor model performance in production environments, implementing robust logging and alerting systems.
- Conduct rigorous code reviews and contribute to the technical roadmap of the AI division.
Qualifications
- Master’s or PhD degree in Computer Science, Mathematics, Statistics, or a related technical field (or equivalent practical experience).
- Strong proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow.
- Proven experience in building and deploying Large Language Models (LLMs) or Generative AI applications.
- Deep understanding of ML fundamentals: neural networks, optimization, regularization, and statistical modeling.
- Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).
- Excellent problem-solving skills and the ability to work independently in a fast-paced startup environment.