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ML Inference Engineer – Terminal-Bench Task Designer (vLLM / SGLang / TensorRT-LLM) at Toptal. Location Information: Anywhere in the World. 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. Please mention the word **ASTONISHED** and tag RMzQuMTIxLjIzNS4xODc= when applying to show you read the job post completely (#RMzQuMTIxLjIzNS4xODc=). This is a beta feature to avoid spam applicants. Companies can search these words to find applicants that read this and see they're human..