ML Infrastructure Engineer

Scaled Cognition is developing a new generation of rational, controllable AI models deployable as domain experts for grounded, real-world applications.

As a ML Infrastructure Engineer at Scaled Cognition you will:

• Design and develop the GPU infrastructure that powers our AI models.
• Ensure that our infrastructure can scale.
• Collaborate with research scientists and product engineers to streamline the end-to-end process from model development to production deployment.

Example projects could include:

• Automated GPU Provisioning: Design an automated provisioning system to dynamically allocate GPUs based on workload demands.
• Benchmarking Pipeline: Develop a pipeline for running benchmarking experiments, enabling comparative analysis of model performance.
• Experiment Analysis: Build monitoring and analysis tools to track experiment progress, monitor performance metrics, and visualize results.

You might be the right person for the job if you:

• Are a continuous learner and are eager to explore new tools and technologies.
• Thrive in a dynamic environment and are comfortable adapting to changing priorities while maintaining a focus on delivering high-quality solutions.
• Have successfully navigated projects with significant product and technical ambiguity, and you excel at the intersection of complex technical challenges and user-focused solutions.

Preferred Qualifications:

• Prior experience designing and implementing GPU infrastructure.
• Experience with ML Ops tools such as MLflow, TensorBoard, or Weights & Biases.
• A strong sense for scalability and developing secure, highly reliable environments.