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Sparkinity

AI Research Engineer

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Description

We're seeking an AI Research Engineer to own model evaluations end-to-end, turning ambiguous capabilities into rigorous, reproducible benchmarks. Working closely with researchers, you will design datasets, analyze model behavior at scale, and directly shape the training lifecycle.


Responsibilities:

  • Design rigorous benchmarks for reasoning, coding, and agentic capabilities across C/C++, Rust, embedded systems, hardware specifications, and engineering tasks.
  • Develop deterministic evaluators using compilers, emulators, simulators, static analysis, formal verification, and hardware-in-the-loop systems.
  • Build distributed infrastructure to run evaluations reliably against models and live training checkpoints.
  • Diagnose regressions and anomalous results, separating model failures from issues in prompts, data, evaluators, or infrastructure.
  • Design robust metrics, difficulty curricula, contamination-resistant tasks, and experiments around prompting, sampling, and scaffolding.
  • Turn evaluation failures into targeted datasets, reward signals, and new training objectives in collaboration with RL researchers.
  • Create dashboards, experiment tracking, and evaluation libraries that make model performance easy to understand and trust.
  • Own the development of cutting-edge infrastructure from day one.
  • Solve complex engineering challenges that directly shape our product roadmap.
  • Accelerate your career alongside elite researchers in a fast-paced environment.
  • Work closely with a brilliant, collaborative team of industry pioneers.


Requirements:

  • Strong Python programming and software engineering fundamentals.
  • Experience building benchmarks, evaluation systems, automated graders, research infrastructure, or similar testbeds.
  • Strong understanding of LLMs and modern evaluation methodologies.
  • Strong analytical and experimental thinking; you care deeply about whether a metric actually measures the intended capability.
  • Experience debugging complex systems and investigating unexpected experimental results.
  • Strong written and verbal communication.

Skills

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