Overview
We are building a benchmark that tests how well AI agents handle real expert work in a terminal. We are hiring ML inference and serving experts to turn real, paid tasks from their jobs into expert-level Terminal-Bench challenges. Each task includes the environment, a working solution, and an automated check that proves the result is correct.
THIS IS A TASK BASED COMPENSATION MODEL. PLEASE CHECK THE APPLICATION QUESTIONS FOR MORE DETAILS.
Responsibilities
• Design expert-level Terminal-Bench tasks that turn real, paid ML inference work from your own experience into challenges an AI agent must solve end to end.
• Define the files, systems and environments each task runs on, such as model serving with streaming, so the challenge is realistic, reproducible and command-line solvable.
• Write a working reference solution for every task and verify it runs end to end, sized for about 60 to 75 minutes of expert effort.
• Build automated checks that objectively prove a result is correct, including cases where ML outputs go wrong silently, such as numerical drift or metric bugs.
• Package tasks with Docker and Git so they are reproducible, and document the systems, tools, data and rules involved so reviewers can validate difficulty.
• Iterate on tasks based on reviewer feedback and agent test runs, adjusting difficulty and fixing ambiguities so each task stays expert-level, fair and objectively gradable.
Skills/Experience
• 3+ years doing ML inference or serving work hands-on (not only studying, selling or managing it), or a PhD plus active research in the subfield
• Did this work in the last 3 years and can name the systems, tools, data and rules used
• Trained, served or evaluated models at real scale (multi-GPU or production serving), not only notebooks or API calls
• Hands-on with PyTorch or JAX internals and serving stacks such as vLLM, SGLang or TensorRT-LLM
• Can explain how ML results go wrong without errors (numerical drift, metric bugs) and how to catch them
• Technical fluency in Python and the command line, and with files, CSV/JSON, APIs or databases
• Availability to start by early November 2026 and complete tasks of about 60 to 75 minutes each
Nice-to-have
• AI lab or LLM infrastructure experience
• Contributions to PyTorch, vLLM or SGLang
• GPU profiling experience
• Docker and Git proficiency
• Experience writing automated tests
Process
• Task vetting instead of an interview: complete a vetting task so the client can assess your task design
• Paid per task; each task takes about 60 to 75 minutes
• Work is delivered task by task, with reviewer feedback on submissions
• Expected start in early November 2026

Overview
We are building a benchmark that tests how well AI agents handle real expert work in a terminal. We are hiring ML inference and serving experts to turn real, paid tasks from their jobs into expert-level Terminal-Bench challenges. Each task includes the environment, a working solution, and an automated check that proves the result is correct.
THIS IS A TASK BASED COMPENSATION MODEL. PLEASE CHECK THE APPLICATION QUESTIONS FOR MORE DETAILS.
Responsibilities
• Design expert-level Terminal-Bench tasks that turn real, paid ML inference work from your own experience into challenges an AI agent must solve end to end.
• Define the files, systems and environments each task runs on, such as model serving with streaming, so the challenge is realistic, reproducible and command-line solvable.
• Write a working reference solution for every task and verify it runs end to end, sized for about 60 to 75 minutes of expert effort.
• Build automated checks that objectively prove a result is correct, including cases where ML outputs go wrong silently, such as numerical drift or metric bugs.
• Package tasks with Docker and Git so they are reproducible, and document the systems, tools, data and rules involved so reviewers can validate difficulty.
• Iterate on tasks based on reviewer feedback and agent test runs, adjusting difficulty and fixing ambiguities so each task stays expert-level, fair and objectively gradable.
Skills/Experience
• 3+ years doing ML inference or serving work hands-on (not only studying, selling or managing it), or a PhD plus active research in the subfield
• Did this work in the last 3 years and can name the systems, tools, data and rules used
• Trained, served or evaluated models at real scale (multi-GPU or production serving), not only notebooks or API calls
• Hands-on with PyTorch or JAX internals and serving stacks such as vLLM, SGLang or TensorRT-LLM
• Can explain how ML results go wrong without errors (numerical drift, metric bugs) and how to catch them
• Technical fluency in Python and the command line, and with files, CSV/JSON, APIs or databases
• Availability to start by early November 2026 and complete tasks of about 60 to 75 minutes each
Nice-to-have
• AI lab or LLM infrastructure experience
• Contributions to PyTorch, vLLM or SGLang
• GPU profiling experience
• Docker and Git proficiency
• Experience writing automated tests
Process
• Task vetting instead of an interview: complete a vetting task so the client can assess your task design
• Paid per task; each task takes about 60 to 75 minutes
• Work is delivered task by task, with reviewer feedback on submissions
• Expected start in early November 2026