Job Description
Are you ready to define the technological landscape of the future? Nexus Future Labs is looking for a visionary Senior Machine Learning Engineer to join our elite R&D team in San Francisco. We are building the foundational AI infrastructure for the next decade, and we need a technical leader who thrives on solving complex, high-stakes problems at scale.
In this pivotal role, you will architect and deploy state-of-the-art deep learning models that power our core products. You will bridge the gap between cutting-edge research and production-grade software, ensuring our AI solutions are not only powerful but also ethical and scalable. If you are passionate about the intersection of data science and software engineering, this is your chance to shape the future.
Responsibilities
- Architect & Deploy: Design, implement, and optimize scalable machine learning pipelines and deep learning models for production environments.
- Collaboration: Work closely with cross-functional teams (data scientists, product managers, and engineers) to integrate AI models seamlessly into our ecosystem.
- Optimization: Continuously monitor and optimize model performance, latency, and resource utilization to ensure a world-class user experience.
- Mentorship: Mentor junior engineers and data scientists, providing technical guidance and fostering a culture of innovation and best practices.
- Research: Stay ahead of industry trends, experiment with new algorithms, and prototype innovative solutions to drive competitive advantage.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field.
- Experience: 5+ years of professional experience in software engineering or machine learning, with a strong focus on deep learning.
- Programming: Expert-level proficiency in Python, with deep knowledge of frameworks such as TensorFlow, PyTorch, or JAX.
- Infrastructure: Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- MLOps: Demonstrable experience implementing MLOps workflows, CI/CD pipelines, and model monitoring tools.