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Pathway

Frontier AI research for continuous learning

AI Benchmark & Datasets Engineer / Researcher

Natural Language Processing & LLMsUpdated on Nov 14
Full-time€100k

About the Job

Requirements

  • Meet at least one of these criteria: have published at least one paper at NeurIPS, ICLR, or ICML as lead author or with significant conceptual and code contributions; have significantly contributed to a newsworthy LLM training effort; have worked for at least six months at a leading machine-learning research center; or have been an ICPC World Finalist or an IOI, IMO, or IPhO high-school medalist.
  • Have experience with machine-learning or large language model evaluation, data science, or technical product work, ideally involving benchmarks or experimentation.
  • Be comfortable reading research papers, leaderboards, and GitHub repositories, and converting them into clear, repeatable benchmark specifications.
  • Be able to communicate with engineers and customers and translate technical details into business value.
  • Care about high-quality data, reproducible experiments, and clear documentation.
  • Be fluent in English.

Responsibilities

  • Identify, prioritize, and curate public and client-driven benchmarks relevant to target use cases and markets.
  • Evaluate candidate benchmarks for clarity, data quality, evaluation methodology, and fit with the model roadmap.
  • Run benchmarks with baseline models to validate setup, uncover edge cases, and reduce risk for research and development runs.
  • Deliver benchmark-ready packages to research and development, including specifications, data, evaluation scripts, expected metrics, and constraints.
  • Maintain shared vocabulary and documentation for benchmarks, datasets, and evaluation formats for go-to-market and research and development teams.
  • Track and organize benchmark results, model leaderboards, and definitions of success across customers and scenarios.
  • Contribute to demos and public-facing proof points based on benchmark outcomes.
  • Define and drive the benchmarking process for AI model evaluation.

Desired Qualifications

  • Published or open-sourced work on large language model evaluation, benchmarking, or data quality.
  • Experience designing custom benchmarks or evaluation protocols for novel model capabilities.

About the Company

Pathway

Frontier AI research for continuous learning

Pathway conducts frontier AI research building post-transformer architectures for continuous, real-time learning. Its core BDH (Baby Dragon Hatchling) is a biologically inspired state-space model with persistent memory for specialized reasoning in finance, tech, and healthcare. BDH enables ongoing learning and real-time inference without periodic retraining, differing from standard transformer systems that rely on static weights. The goal is to scale BDH development and compute infrastructure to deploy continuous-learning, domain-specific reasoning models across industries.

Company size
51-200
Company stage
Seed
Total funding
$14.6M
Headquarters
Palo Alto, CA
Founded
2020

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