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ML Systems Engineer AI Trainer
Summary by AIEngineer.careers
ML Systems Engineer AI Trainer is a MLOps Engineer role (full-time). with Mercor. in REMOTE, CHICAGO, US. Compensation shown: $190K–$253K (est.). Imported listing (source: drjobpro.com). Apply on the employer's site (drjobpro.com).
Imported listing (source: drjobpro.com) · Apply on drjobpro.com
REMOTE · CHICAGO · US
$190K–$253K (est.)
Full-time
MID
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from drjobpro.com
Mercor is seeking an MLOps Engineer specializing in LLM systems to design challenging tasks and solutions for GPU kernel performance profiling and inference serving. The role involves guiding research teams, evaluating ML infrastructure tasks, and developing technical guidelines for optimization and latency trade-offs. This is a contract position requiring strong expertise in modern accelerators and deep learning frameworks.
Responsibilities
- Design challenging tasks across GPU kernels, performance profiling, debugging, and inference serving.
- Write accurate, well-structured technical solutions and provide clear written feedback.
- Guide research and engineering teams to improve AI model performance on ML systems.
- Develop guidelines and rubrics covering kernel-level optimization and profiler output interpretation.
Requirements
- 2+ years of hands-on professional experience in ML systems, ML infrastructure, or GPU performance engineering.
- Practical experience with GPU kernels (e.g., CUDA, Triton) or performance profiling tools (e.g., Kineto).
- Working production experience with JAX and/or PyTorch.
- Familiarity with modern accelerators such as A100, H100, or TPU.
- Strong written communication skills.
Mercor
San Francisco · US · 10149+ employees
Mercor is an artificial intelligence company that organizes human intelligence to power the AI economy. It provides a platform that connects domain experts with AI labs and enterprises to train frontier models, deploy custom AI agents, and encode organizational knowledge into AI systems.
