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ML Inference Engineer - Terminal-Bench Task Designer (vLLM / SGLang / TensorRT-LLM)

REMOTE JOBS LLC
United States
3 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
3 years minimum
Working hours
Regular working hours
Job source
www.jobmonkeyjobs.com

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Automation of Tests Command-Line Interface Databases JSON Python (Programming Language) Pytorch Large Language Models Git SGLang Machine Learning Operations
+5 more
TensorRT Virtual Agents VLLM Docker Web Api

Job description

  • 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.

Requirements

  • 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

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.jobmonkeyjobs.com
Prepare application

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