ML Inference Engineer – Terminal-Bench Task Designer

Location not specified•Remote

RemoteUSDweworkremotely

Full-Stack Programming

About this role

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 Skills/Experience Nice-to-have Process

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.
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

Posting details

Remote

Yes

Employment type

—

Workplace type

Remote

Source

weworkremotely

First seen

11/5/2026, 8:10:05 AM

Last seen

11/5/2026, 8:10:05 AM