Pulse is tackling one of the most persistent challenges in data infrastructure: extracting accurate, structured information from complex documents at scale. Our breakthrough architecture combines schema mapping with fine-tuned extraction models where legacy OCR and parsing consistently fail.
We’re a small, fast-growing team in San Francisco powering Fortune 100 enterprises, YC startups, public investment firms, and growth-stage companies. We’re backed by tier-1 investors and scaling quickly.
As a Machine Learning Engineer Intern, you’ll work directly with our founding engineers on core ML challenges at the intersection of computer vision, NLP, and data infrastructure. This internship is designed for second- or third-year undergraduate students eager to gain hands-on experience in production-scale AI systems.
Train and fine-tune OCR, layout, table, and vision-language models
Contribute to evaluation, data curation, and active learning pipelines
Optimize inference, batching, and quantization on GPU
Collaborate with engineers to productionize models with reliability in mind
Document findings that inform the model roadmap
Currently an undergraduate student in Computer Science, Engineering, or a related field
Strong experience with Python and PyTorch or JAX
Familiarity with modern vision or multimodal architectures
Solid programming skills and interest in production systems
Nice to Have
Experience with distributed training or model optimization (Triton, TensorRT, ONNX)
Open source contributions in ML/NLP/CV
$40–$70 per hour (depending on experience)
Daily meal stipend, office perks, and close mentorship from the founding team
Pulse