Summer 2025

Machine Learning Engineering Intern

Posted on 2/12/2025

Lamini AI

Lamini AI

1-10 employees

Offers enterprise-grade LLMs tailored to data

No salary listed

Menlo Park, CA, USA

This position is onsite in Menlo Park.

This position is onsite in Menlo Park.

This position is onsite in Menlo Park.

Category
AI & Machine Learning (2)
,
Required Skills
Microsoft Azure
Python
Tensorflow
Pytorch
Machine Learning
AWS
Google Cloud Platform

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Requirements
  • Currently pursuing a Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Data Science, or a related field.
  • Strong programming skills in Python.
  • Familiarity with deep learning frameworks such as PyTorch or TensorFlow.
  • Understanding of NLP concepts and transformer-based architectures (e.g., GPT, BERT, LLaMA).
  • Experience with data preprocessing, ETL pipelines, or large-scale dataset management.
  • Basic knowledge of cloud platforms (AWS, GCP, or Azure) and distributed computing.
Responsibilities
  • Design, develop, and optimize data pipelines for large-scale LLM training.
  • Process and clean large datasets for pretraining and fine-tuning language models.
  • Assist in implementing distributed training strategies for LLMs.
  • Experiment with data augmentation and preprocessing techniques to enhance model performance.
  • Work with frameworks like PyTorch, TensorFlow, and Hugging Face Transformers.
  • Monitor and debug training runs, optimizing computational efficiency.
  • Collaborate with cross-functional teams to integrate data pipelines into production workflows.
Desired Qualifications
  • Experience working with LLM fine-tuning, LoRA, or adapter-based training.
  • Hands-on experience with ML model training and hyperparameter tuning.
  • Familiarity with tools like Hugging Face Datasets, Ray, or Apache Spark.
  • Exposure to containerization (Docker, Kubernetes) and ML model deployment.

Lamini AI builds and operates tools for enterprises to create, train, and deploy customized language models using their own data. Its main offering is an LLM engine delivered as a service, plus a library and API that software engineers can use to build, fine-tune, and ship AI models quickly. How it works: clients fine-tune models on their data (including RLHF) via Lamini’s API, while Lamini handles hosting and compute so new model versions can be released without managing infrastructure. Lamini differentiates itself by enabling clients to own and tailor models to their specific workflows, rather than relying on generic off-the-shelf models, and by providing an end-to-end, data-driven workflow library for rapid model development and deployment. The company’s goal is to make generative AI accessible and customizable for enterprises, improving automation, software development productivity, and the effective use of proprietary data.

Company Size

1-10

Company Stage

Series A

Total Funding

$25M

Headquarters

Menlo Park, California

Founded

2022

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Simplify Jobs

Simplify's Take

What believers are saying

  • Fortune 500 enterprises adopt Lamini to fine-tune proprietary data on Llama 3.
  • Scales dynamically to 1,000+ GPUs across on-premises and cloud infrastructure.
  • Team pioneered LLM scaling laws and mentored leaders behind GPT-4, Claude, Llama.

What critics are saying

  • OpenAI and Anthropic commoditize fine-tuning; Lamini's differentiation erodes in 12–18 months.
  • AWS SageMaker, Vertex AI, Azure bundle equivalent LLM tuning at lower cost.
  • Loss of 2–3 Fortune 500 customers eliminates 30–50% of revenue.

What makes Lamini AI unique

  • Memory Tuning reduces hallucinations by 95% through specialized expert models.
  • Full LLM tuning stack orchestrates GPU workloads across AMD and NVIDIA.
  • Deploys factual LLMs in secure air-gapped environments within 10 minutes.

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