We are currently partnered with a well-backed AI Safety startup who are on a mission to improve humanity's understanding of complex AI models and improve the safety and reliability of AI on a global scale. The Founding team consists of experienced, successful entrepreneurs and pioneering leaders from some of the world's top Research Labs.
They are currently looking for Staff Machine Learning Engineer to join team, with a focus around building scalable machine learning infrastructure, deploying state-of-the-art models, and optimizing algorithms to handle extremely large and complex models. You will work closely with research scientists and product engineers to design and implement systems that will power the next generation of AI safety solutions.
You will ultimately be responsible for the design, build, and maintenance of scalable machine learning infrastructure for deploying large models in production environments, and will optimize algorithms and pipelines to efficiently handle extremely large, complex models, ensuring robustness, scalability, and performance.
Qualifications required:
5+ Years in ML / Research Engineering ideally within startup environments
Experience building + scaling ML infrastructure for large, complex models in production
Hands-on experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
Familiarity with MLOps practices, including continuous integration, deployment, and monitoring of ML models.
We are speaking with individuals as a priority for this hire - please apply ASAP for more info.