Job Description
Are you ready to architect the intelligence of tomorrow? Nexus Future Labs is seeking a visionary AI/ML Research Engineer to lead our strategic initiatives for the year 2026 and beyond. We are building the foundational models that will redefine human-machine interaction, and we need a pioneer who can bridge the gap between theoretical research and scalable production systems.
In this role, you won't just be maintaining the status quo; you will be pushing the boundaries of Generative AI, Reinforcement Learning, and Neural Architecture Search. If you are passionate about solving unsolved problems and have a track record of excellence in deep learning, we want to meet you.
Why Join Us?
We offer a competitive compensation package, equity options, and the unique opportunity to work on cutting-edge technology that will shape the future of the industry. Our office in the heart of San Francisco is designed for collaboration and innovation.
Responsibilities
- Lead the research and development of state-of-the-art Large Language Models (LLMs) and multimodal systems.
- Design and implement novel training algorithms to improve model efficiency, accuracy, and inference speed.
- Collaborate with cross-functional engineering teams to translate research breakthroughs into scalable production infrastructure.
- Conduct rigorous experimentation and A/B testing to validate new model architectures and training methodologies.
- Mentor junior researchers and engineers, fostering a culture of continuous learning and technical excellence.
- Stay abreast of the latest academic literature to integrate novel techniques into our proprietary frameworks.
Qualifications
- PhD or Master’s degree in Computer Science, Artificial Intelligence, or a related quantitative field.
- 5+ years of professional experience in Machine Learning, Deep Learning, or Natural Language Processing.
- Strong proficiency in Python, PyTorch, or TensorFlow with a deep understanding of distributed training frameworks.
- Proven track record of publishing research in top-tier conferences (NeurIPS, ICML, ACL) or open-sourcing impactful models.
- Experience with model optimization, quantization, and serving at scale.
- Excellent problem-solving skills and the ability to thrive in a fast-paced, ambiguous startup environment.