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Pathway

Frontier AI research for continuous learning

Machine Learning Researcher / Engineer - Foundation Models

AI Research +1Updated on Nov 14
Full-time

About the Job

Requirements

  • Meet at least one of the following 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 large language model training effort; have worked for at least six months in a leading machine learning research center; or have been an ICPC World Finalist or an IOI, IMO, or IPhO high-school medalist.
  • Have a deep learning research background with a track record in language models and/or reinforcement learning; candidates with a vision or robotics machine learning background are also welcome.
  • Be interested in improving foundational architectures and creating new benchmarks.
  • Have hands-on experience with experiments and model training using PyTorch, JAX, or TensorFlow.
  • Understand GPU architecture, memory design, communication, and graph algorithms.
  • Have familiarity with model monitoring, Git, build systems, and CI/CD.
  • Be fluent in English.

Responsibilities

  • Perform distributed model training.
  • Improve or adapt model architectures based on experiment results.
  • Design new tasks and experiments.
  • Optionally oversee activities of team members involved in data preparation.

Desired Qualifications

  • Have knowledge of approaches used in distributed training.
  • Be familiar with Triton.
  • Have a successful track record in algorithms and data science contests.
  • Provide a code portfolio.

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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