Job Type: Full Time

Location:

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

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

To apply: https://weworkremotely.com/remote-jobs/toptal-ml-inference-engineer-terminal-bench-task-designer

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