ML Systems Engineer - Fully Remote

• Design challenging tasks across GPU kernels, performance profiling, debugging, and inference serving. Write accurate, well-structured solutions.
• Guide research and engineering teams to close knowledge gaps and improve AI model performance on ML systems and training infrastructure.
• Evaluate MLOps and ML systems tasks. Provide clear, written technical feedback that stands up to reviewer scrutiny.
• Develop guidelines and detailed rubrics covering kernel-level optimization, profiler output interpretation, and serving throughput and latency trade-offs.
• Collaborate with other subject matter experts to ensure training data consistency and accuracy., PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.

• 2+ years of hands-on professional experience in ML systems, ML infrastructure, or GPU performance engineering.
• Practical experience in GPU kernels (e.g., CUDA, Triton) or performance profiling (e.g., Kineto, torch.profiler).
• Working production experience with JAX and/or PyTorch.
• Familiarity with modern accelerators such as A100, H100, or TPU.
• Strong written communication skills.

Audible, Inc.

• Newark, NJ At Audible, we believe stories have the power to transform lives. It’s why we work with some of the world’s leading creators to produce and share audio storytelling with our millio…