ML Inference & GPU Kernel Research Engineer
Listing checked October 5, 2026 · pay as published by AfterQuery
Overview
AfterQuery builds a research cohort of senior ML inference and GPU kernel specialists to test how frontier AI models handle expert-level problems. You will not ship production services. Instead, you design hard scenarios, write reference answers and grading rubrics, then judge model outputs for technical accuracy and domain judgment. The work centers on ML inference, GPU kernel engineering, and large-model deployment, with schedules that stay asynchronous and remote. Candidates with 6 to 10 years in the field and a PhD, or a master's plus a strong research record, who send the requested materials get priority review.
What You'll Do6
- 1Create realistic problem sets that mirror hard challenges in ML inference serving, kernel optimization, and large-model deployment.
- 2Write expert reference solutions that show step-by-step reasoning, tradeoffs, and correct implementation choices.
- 3Build grading rubrics that separate solid answers from weak ones across correctness, depth, and domain judgment.
- 4Review AI-generated responses and mark errors in logic, math, code, or system design.
- 5Calibrate standards with other domain experts in the research cohort so evaluations stay consistent.
- 6Document feedback on model outputs in clear written form for research teams.
Requirements8
- 16 to 10 years of hands-on experience in machine learning, ML inference, or GPU kernel work.
- 2PhD or doctorate in a relevant field, or a master's degree paired with a strong research record.
- 3Active involvement in the field through recent industry or research work, not only academic-adjacent activity.
- 4Strong technical writing skills for authoring explanations, reference solutions, and structured feedback.
- 5Ability to work on your own in an asynchronous remote setup.
- 6Preferred: published research, open-source contributions, or recognized work in inference serving, kernel optimization, or model deployment.
- 7Preferred: prior experience reviewing, grading, or editing other engineers' technical work.
- 8Preferred: direct use of SGLang, vLLM, Mamba or Mamba2, TensorRT-LLM, or GPU kernel optimization.
Who Should Apply
The right candidate brings 6 to 10 years of active work in ML inference, kernel engineering, or large-model deployment and can point to a PhD, or a master's plus published or open-source research. You enjoy writing detailed technical explanations, building rubrics, and judging whether an AI answer shows real domain judgment. This role suits specialists who want evaluation and research work instead of production engineering and can manage an asynchronous remote schedule. It fits poorly if you have never reviewed or graded another engineer's work, or if your experience sits in academic-adjacent projects without hands-on system building. Candidates who claim skills they cannot support may fall below the 70% qualifications threshold, and AfterQuery runs background checks that can flag misrepresented experience.
Salary Insight
This listing pays $135.00 per hour. The broader range shown by AfterQuery runs from $100 to $170 per hour and tracks experience, so this rate sits in the middle of the band for senior ML inference and GPU kernel experts. Expect the top end to favor candidates with deep kernel work, publication records, or open-source contributions in serving systems.
Pay and demand for AI Training roles
AggregatedTypical pay
$70/hour
This role
$135/hr
Most AI Training roles pay $40–$98 per hour. This role's pay sits above that range.
Based on 1,035 similar roles that publish pay · 302 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
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Most requested skills · share of roles
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- data annotation9%
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Compensation
$135/hr
Required Skills
Application Tip
Lead your application with proof of hands-on inference or kernel work: name specific systems like SGLang, vLLM, TensorRT-LLM, or Mamba, describe problems you solved, and include one writing sample such as a rubric, code review, or research summary. Quantify tenure and results to show you clear the 6 to 10 year bar and meet at least 70% of the qualifications before background checks begin.
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