Software/ML Engineer - LLM Inference
Listing checked October 5, 2026 · pay as published by AfterQuery
Overview
AfterQuery needs senior software and ML engineers to define how well frontier models reason about LLM inference, GPU kernel design, and model-serving architecture. You will craft evaluation scenarios and reference answers for real systems-engineering problems, then assess model outputs against those benchmarks. The contract runs remote at 3 hours per week, so you can schedule work around another job. Work centers on research and evaluation, not shipping production code for AfterQuery.
What You'll Do8
- 1Build realistic problem sets that test how models handle LLM inference optimization.
- 2Create evaluation scenarios around GPU kernel design and systems performance engineering.
- 3Write reference solutions that show correct and strong approaches for each problem.
- 4Grade model outputs against systems-engineering standards drawn from your own practice.
- 5Flag answers that miss performance constraints, produce invalid CUDA, or misread serving architecture tradeoffs.
- 6Refine prompts and rubrics so each scenario targets a clear inference or serving concept.
- 7Document technical explanations that justify your grading decisions.
- 8Align quality bars with other senior engineers who review the same model outputs.
Requirements7
- 1Bachelor's degree or higher in computer science, computer engineering, electrical engineering, or a related technical field.
- 2Hands-on depth in at least one area: GPU inference optimization, CUDA kernel development, model-serving systems such as vLLM, TensorRT-LLM, or SGLang, quantization, or optimizer design.
- 3Systems-engineering ownership from a recognized technology or AI company.
- 4Clear technical writing skills for authoring explanations and feedback.
- 5Preferred: direct work with SGLang, Mamba or Mamba2 architectures, or IBM Granite models.
- 6Preferred: contributions to open-source inference or serving projects like vLLM, SGLang, or TensorRT-LLM.
- 7Preferred: distributed inference experience with tensor parallelism, expert parallelism, continuous batching, or KV-cache optimization.
Who Should Apply
The ideal candidate has owned GPU inference or model-serving systems at a technology or AI company and can discuss tradeoffs in kernels, quantization, or distributed execution. Strong writers who enjoy explaining why a solution works or fails will fit the evaluation side of this contract. People who only build high-level ML models and avoid low-level performance work will find the problems out of scope. A frequent reason candidates get rejected is listing model training projects without showing hands-on work in inference optimization, CUDA, or serving frameworks. Another low-fit signal is weak technical writing, since the role requires detailed feedback and reference solutions rather than code alone.
Salary Insight
The contract pays $100 to $150 per hour. For part-time expert evaluation work in AI systems, that range matches senior engineers who bring direct CUDA, serving, or distributed inference experience. The weekly commitment is 3 hours, so total earnings depend on how many contracts you take on.
Pay and demand for Machine Learning & AI roles
AggregatedTypical pay
$80/hour
This role
$125/hr
Most Machine Learning & AI roles pay $55–$105 per hour. This role's pay sits above that range.
Based on 484 similar roles that publish pay · 93 publish only a top rate; those count at the rate they gave
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Most requested skills · share of roles
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- ai evaluation8%
- ai training8%
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Compensation
$125/hr
Required Skills
Application Tip
Lead with one inference system you owned end to end, naming the framework (vLLM, SGLang, or TensorRT-LLM) and the kernel, quantization, or distributed optimization you changed. Add a short writing sample that explains a systems tradeoff, because the role grades model outputs and needs clear technical prose.
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