ML Inference Engineer – Terminal-Bench Task Designer

Toptal; United States; Remote; USD 165,000 - 248,000; Full time

Job summary

Toptal is seeking an ML inference and serving expert to design and deliver terminal-based benchmarks that mimic real paid ML work. You will define tasks, environments, and reference solutions that are end-to-end runnable by review teams, with time estimates of 60 to 75 minutes per task.

Responsibilities include building automated checks, packaging tasks with Docker and Git, and documenting data, tools, and rules so reviewers can validate difficulty and fairness.

Qualifications

• 3+ years doing ML inference or serving work hands-on (not only studying, selling or managing it).
• Trained, served or evaluated models at real scale (multi-GPU or production serving).
• Hands-on with PyTorch or JAX internals and serving stacks such as vLLM, SGLang or TensorRT-LLM.

Responsibilities

• Design expert-level Terminal-Bench tasks from real ML inference work.
• Define environments and streaming serving details for realistic challenges.
• Write a working reference solution and verify end-to-end execution (60–75 minutes).
• Build automated checks to prove results are correct.
• Package tasks with Docker and Git, and document systems and rules.

Skills

ML inference/serving

PyTorch/JAX internals

Python

Command line

Documentation writing

Education

PhD in ML (optional)

Tools

Docker

Git

Job description

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