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Senior ML/Software Engineer – LLM Systems at AfterQuery

posted 1 hour ago
experts.afterquery.com Contractor remote 100-150/h 27 views

Senior ML/Software Engineer – LLM Inference & Systems Optimization | $100–150/hr | Worldwide Remote

AFTERQUERY is assembling an elite group of senior software and ML engineers to tackle one of the most challenging open problems in AI: evaluating how well frontier models reason about LLM inference systems, GPU-level performance optimization, and model-serving architecture. This is a unique research-and-evaluation role — not a traditional engineering position — where your deep expertise shapes what correct and excellent look like for AI models solving the same class of problems you work on every day.

What You'll Do

  • Design rigorous evaluation scenarios based on real-world LLM inference and systems-engineering challenges
  • Author reference solutions and detailed technical explanations for complex GPU and model-serving problems
  • Grade and provide structured feedback on AI model outputs against expert-level benchmarks
  • Help define quality standards for AI reasoning in systems optimization domains

Required Qualifications

  • 5–10 years of professional software engineering or ML systems experience
  • Bachelor's degree or higher in Computer Science, Computer Engineering, Electrical Engineering, or a related field
  • Strong hands-on background in one or more of: GPU inference optimization, custom CUDA kernel development, model-serving systems (e.g., vLLM, TensorRT-LLM, SGLang), quantization, or optimizer design
  • Experience at a recognized technology or AI company with real systems-engineering ownership
  • Excellent written communication skills — you'll be authoring technical explanations and feedback, not just code

Compensation & Details

  • Rate: $100–$150/hr
  • Engagement: Contract / Freelance
  • Location: Fully Remote (Worldwide)
  • Department: Software Engineering

If you're a seasoned ML or systems engineer who wants to directly influence how AI understands the hardest problems in your field, we'd love to hear from you.

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