Headquarters:
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
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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.
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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.
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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.
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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.
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Package tasks with Docker and Git so they are reproducible, and document the systems, tools, data and rules involved so reviewers can validate difficulty.
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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
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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
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Did this work in the last 3 years and can name the systems, tools, data and rules used
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Trained, served or evaluated models at real scale (multi-GPU or production serving), not only notebooks or API calls
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Hands-on with PyTorch or JAX internals and serving stacks such as vLLM, SGLang or TensorRT-LLM
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Can explain how ML results go wrong without errors (numerical drift, metric bugs) and how to catch them
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Technical fluency in Python and the command line, and with files, CSV/JSON, APIs or databases
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Availability to start by early November 2026 and complete tasks of about 60 to 75 minutes each
Nice-to-have
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AI lab or LLM infrastructure experience
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Contributions to PyTorch, vLLM or SGLang
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GPU profiling experience
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Docker and Git proficiency
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Experience writing automated tests
Process
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Task vetting instead of an interview: complete a vetting task so the client can assess your task design
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Paid per task; each task takes about 60 to 75 minutes
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Work is delivered task by task, with reviewer feedback on submissions
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Expected start in early November 2026
To apply: https://weworkremotely.com/remote-jobs/toptal-ml-inference-engineer-terminal-bench-task-designer
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