About the job
At Pulse, we are revolutionizing the way data infrastructure functions by addressing one of the most enduring challenges: the extraction of precise, structured information from complex documents at scale. Our innovative architecture integrates advanced schema mapping with finely-tuned extraction models, overcoming the limitations of traditional OCR and parsing techniques.
As a dynamic and rapidly expanding team located in San Francisco, we empower Fortune 100 enterprises, YC startups, public investment firms, and growth-stage companies. Our impressive growth is supported by top-tier investors who believe in our vision.
About the Internship
Join us as a Machine Learning Engineer Intern, where you'll collaborate closely with our founding engineers to tackle core ML challenges that blend computer vision, natural language processing (NLP), and data infrastructure. This internship is tailored for ambitious second- or third-year undergraduate students eager to acquire hands-on experience with production-scale AI systems.
Key Responsibilities
Train and optimize OCR, layout, table, and vision-language models.
Contribute to model evaluation, data curation, and active learning processes.
Enhance inference, batching, and quantization techniques on GPU.
Collaborate with engineers to ensure reliable productionization of models.
Document insights that guide the future of our model roadmap.
Required Qualifications
Currently pursuing a degree in Computer Science, Engineering, or a related discipline.
Proficiency in Python and experience with frameworks such as PyTorch or JAX.
Familiarity with contemporary vision or multimodal architectures.
Strong programming skills with a keen interest in production systems.
Preferred Qualifications
Experience with distributed training or model optimization tools (e.g., Triton, TensorRT, ONNX).
Contributions to open-source projects in ML, NLP, or CV.
Compensation
$40–$70 per hour based on experience.
Includes a daily meal stipend, office perks, and mentorship from our founding team.

