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Pulse

APIs for AI-powered document extraction

Machine Learning Engineer

Applied Machine Learning +1Confirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA

About the Job

Requirements

  • At least 3 years of experience in applied machine learning or research, or a strong open-source record.
  • Experience with PyTorch or JAX and modern vision or multimodal architectures.
  • Solid engineering discipline and a metrics focus.

Responsibilities

  • Train and fine-tune optical character recognition, layout, table, and vision-language models.
  • Build evaluation, data curation, and active learning pipelines.
  • Optimize inference, batching, and quantization on graphics processing units.
  • Productionize models with clear service-level agreements and rollback plans.
  • Write internal notes that inform model and product roadmaps.

Desired Qualifications

  • Prior startup or founding experience.
  • Experience with Triton Inference Server, TensorRT, ONNX, or distributed training.

About the Company

Pulse

APIs for AI-powered document extraction

Pulse AI provides a cloud service and API suite for AI-powered data extraction and retrieval from unstructured documents. It automates the entire document workflow—from ingestion and structured data extraction to seamless integration with existing databases—supporting formats like PDFs, Word, and Excel and converting information into formats suitable for Large Language Models. This enables building AI agents that can autonomously analyze new documents, extract key details, and continually update a knowledge base. Pulse AI differentiates itself by offering end-to-end workflow automation, a flexible pricing model, and ready-to-use retrieval capabilities that work across industries, with initial focus on supply chain and complex financial documents. The company's goal is to help businesses keep their knowledge bases current and actionable by turning document data into ongoing, AI-ready insights.

Company size
11-50
Company stage
Seed
Total funding
$4M
Headquarters
San Francisco, CA
Founded
2024

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