Job Description
Are you ready to architect the next generation of intelligent systems? Nexus Future Tech is seeking a visionary Senior AI/ML Engineer to drive innovation and lead our R&D initiatives aimed at shaping the technological landscape of 2026 and beyond.
In this high-impact role, you will be at the forefront of developing cutting-edge generative AI models, optimizing deep learning architectures, and ensuring our systems are scalable, secure, and ethically sound. We are looking for a problem solver who thrives in a fast-paced, collaborative environment and is passionate about pushing the boundaries of what is possible.
Why Join Us?
- Work with state-of-the-art technology and industry-leading experts.
- Competitive compensation package with equity options.
- Flexible remote-first policy with a hub in the heart of San Francisco.
- Focus on long-term projects that define the future of AI.
Responsibilities
- Design, develop, and deploy scalable machine learning models and algorithms with a focus on Generative AI and Large Language Models.
- Lead the end-to-end machine learning lifecycle, from data ingestion and preprocessing to model training, evaluation, and productionization.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate business requirements into technical solutions.
- Optimize existing models for speed, accuracy, and resource efficiency to support real-time applications.
- Conduct rigorous A/B testing and performance analysis to validate model improvements.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
- Stay abreast of the latest research in AI/ML and integrate relevant advancements into our product roadmap.
Qualifications
- Master’s or PhD degree in Computer Science, Statistics, Mathematics, or a related field (5+ years of relevant experience may substitute for a Master’s).
- Strong proficiency in Python and deep understanding of machine learning frameworks such as PyTorch, TensorFlow, or JAX.
- Extensive experience with natural language processing (NLP) techniques, transformer models, and vector databases.
- Proven track record of deploying large-scale machine learning models to production environments using cloud services (AWS, GCP, or Azure).
- Deep understanding of MLOps practices, CI/CD pipelines, and containerization technologies (Docker, Kubernetes).
- Excellent problem-solving skills and the ability to tackle ambiguous, complex technical challenges.
- Strong communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders.