Job Description
Are you ready to architect the intelligence of tomorrow? Nexus Future Systems is seeking a visionary Senior AI/ML Engineer to lead our next-generation research initiatives. We are building the core infrastructure for the next era of Generative AI and Large Language Models. If you thrive in a high-velocity, innovative environment and want to push the boundaries of what’s possible with Artificial Intelligence, we want to meet you.
In this role, you won’t just maintain existing systems; you will design, train, and deploy state-of-the-art models that will power our products for years to come. Join a team of world-class researchers and engineers dedicated to solving humanity's most complex problems.
Why Join Us?
- Work on cutting-edge LLMs and Computer Vision technologies.
- Competitive compensation and equity packages.
- Unlimited PTO and comprehensive health benefits.
- Flexible remote-first culture with a hub in the heart of SF.
Responsibilities
- Model Development: Design, train, and fine-tune complex deep learning models using Python, PyTorch, and TensorFlow.
- System Architecture: Architect scalable machine learning pipelines and MLOps infrastructure for production deployment.
- Optimization: Continuously optimize model inference latency and resource efficiency to handle millions of requests.
- Cross-Functional Collaboration: Partner with Product Managers and Data Scientists to translate business requirements into technical AI solutions.
- Research & Innovation: Stay at the forefront of AI research, implementing novel architectures and algorithms.
- Code Review & Mentorship: Lead code reviews and mentor junior engineers to foster a culture of technical excellence.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field (or equivalent industry experience).
- Experience: 5+ years of professional experience in Machine Learning and Deep Learning engineering.
- Programming: Expert proficiency in Python and familiarity with C++ for high-performance computing.
- Frameworks: Strong experience with PyTorch, TensorFlow, or JAX.
- Domain Knowledge: Deep understanding of Neural Networks, Transformers, NLP, or Computer Vision.
- Tools: Experience with cloud platforms (AWS/GCP/Azure) and containerization (Docker/Kubernetes).