Description review
ML Inference Engineer – Terminal-Bench Task Designer
Toptal · Anywhere in the World · back to the listing
HR standards
75/100
solid
Title ↔ description
71/100
solid
Reads as
Machine Learning Engineer
87% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- 22 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
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 DETAILS1 .***
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
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 DETAILS1 .***
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