AI / ML Engineer - AI Trainer

ML Research Engineer — Inference & GPU Kernels

Remote

Contract

$135/hr

What you'll need 1 step

• 1 Resume

Job Description

We're building a research cohort of senior domain experts in ML inference and GPU kernel engineering — spanning inference serving systems, kernel optimization, and large-model deployment infrastructure — to help evaluate how frontier AI models reason about hard, real-world expert-level problems. This is research-and-evaluation work, not production engineering: you'll be defining what \"correct\" and \"excellent\" look like on problems you already know deeply.

• Experience: 6–10 years


• Education: PhD/Doctorate, or Master's with a strong research record

Candidates who meet the requirements and provide the requested materials will be prioritized for review. As part of this process, we conduct thorough background checks. Candidates who meet fewer than 70% of the stated qualifications may be flagged for misrepresenting their professional experience, which could affect eligibility for future project staffing.

Why Apply

• Work directly on frontier AI research problems in your area of deep expertise


• Fully remote, flexible, asynchronous — fits around existing work


• Competitive hourly compensation ($100–$170/hr, based on experience)

Responsibilities

• Design realistic technical scenarios and problem sets within your track


• Author expert-level reference solutions and grading rubrics


• Evaluate AI-generated outputs for correctness, depth, and domain judgment

Required Qualifications

• Currently or recently active in the field — hands-on, not purely academic-adjacent


• Strong written communication — you'll be authoring technical explanations and structured feedback, not just doing the work itself


• Comfortable with independent, asynchronous, remote work


• Meets the experience and education bar described in Full Description

Preferred Qualifications

• Prior research publication record, open-source contributions, or recognized work product in your track's domain


• Experience evaluating, reviewing, or grading others' technical work (peer review, code review, grading, editing)


• Track-specific bonus signal ML: SGLang, vLLM, Mamba/Mamba2, TensorRT-LLM, or GPU kernel work

About AfterQuery

AfterQuery is a research lab investigating the boundaries of artificial intelligence through novel datasets and experimentation. We believe great AI comes from exceptional, human-generated data.

We're backed by top investors, including Y Combinator and Box Group, and support all leading AI labs.