# ML Inference Engineer – Terminal-Bench Task Designer (vLLM / SGLang / TensorRT-LLM) > ML Inference Engineer – Terminal-Bench Task Designer (vLLM… at Toptal. Open worldwide: no country, timezone or language lock found by the latest scan. Canonical: https://covesight.com/remote-jobs/toptal-ml-inference-engineer-terminal-bench-task-designer-vllm-sglang-tensorrt-llm-c90bc966/ Last updated: 2026-11-11 Toptal Anywhere in the World [Open worldwide](https://covesight.com/remote-jobs/open-worldwide/) Any timezone No language requirement No salary info Live at last check ## About the 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 • 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 RMmEwOTozZjAwOjMxMDA6MWQ6Og== when applying to show you read the job post completely (#RMmEwOTozZjAwOjMxMDA6MWQ6Og==). 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. Covesight rechecks every listing daily. Last confirmed live: 11 November 2026. ## Cove Score breakdown What our daily check found in this listing. These are the facts behind the Cove Score. **Location** Open worldwide, no country or region limit found **Timezone** Any timezone, no overlap rule found **Language** No requirement found **Salary** Not listed by the company **Freshness** Still live at the last daily check Last confirmed: 11 November 2026 [Spotted something wrong? 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