ML Engineer Intern | Summer 2026

Crustdata (YC F24); San Francisco (CA); On-site; USD 89,280 - 156,240; Full time

Benefits offered by this job

Competitive stipend

Housing stipend for relocation

Direct mentorship from founders

Work reaching real customers

Job summary

A dynamic technology startup in San Francisco is seeking an ML Engineer Intern for a 12-week summer internship. You will work directly with the founding team on groundbreaking ML problems, including structuring multilingual web-scale data and resolving entities across various records. This role promises hands-on experience where your contributions will ship to customers. Ideal candidates are pursuing advanced degrees in relevant fields with strong skills in Python, PyTorch, and NLP. Competitive compensation ranges from $8,000 to $14,000 per month, alongside relocation support.

Qualifications

• Strong fundamentals in NLP, information retrieval, or entity resolution.
• Familiar with transformer architectures and fine-tuned encoder models.
• Experience building retrieval systems, classifiers, or embedding models.

Responsibilities

• Own real ML problems turning multilingual, web-scale data into structured intelligence.
• Resolve entities across different data sources automatically.
• Infer organizational charts from raw people data.

Skills

Python

PyTorch

NLP

Information Retrieval

Entity Resolution

Text Classification

Education

Master's or PhD in Computer Science, Machine Learning, NLP, or related field

Job description

About The Role

Skills: Python, PyTorch, NLP, LLMs, Information Retrieval, Entity Resolution, Text Classification.

We're building the gateway to the internet for AI agents. Our APIs already power hundreds of customers — and we went from 0 to $7M ARR in our first 12 months. Now we need someone who can push the boundaries of what our ML systems can do.

We're hiring an ML Engineer Intern to work directly with our founding team on the research and engineering behind our core intelligence layer. Our platform indexes hundreds of millions of professional profiles and company records from across the web. Making that data searchable, matchable, and enriched is an ML problem at its core.

This is a 12-week summer internship (June–August 2026). You will not be fetching coffee or watching from the sidelines. You will be researching, training, and shipping models — from paper to prototype to production. Previous interns' work has shipped to customers within weeks.

Who you are

• Currently pursuing a Master’s or PhD in Computer Science, Machine Learning, NLP, or a related field
• Strong fundamentals in NLP, information retrieval, or entity resolution — through coursework, research, or side projects
• Familiar with transformer architectures — you've trained or fine-tuned encoder models, not just called APIs
• Experience building retrieval systems, classifiers, or embedding models (in academic or personal projects)
• Exposure to contrastive learning, metric learning, or representation learning
• Have used LLMs for structured extraction, classification, or data generation
• Strong Python and PyTorch
• A true grinder — we work very hard
• Founder mentality — someone who wants to build a company someday

What you'll be doing

You’ll own real ML problems that turn messy, multilingual, web-scale data into structured intelligence. Some example problems:

• A customer searches for "RevOps professionals" — you need to return people titled "Head of Revenue Department," "Revenue Operations Manager," and "VP Sales Operations," across English, French, and German
• Three different data sources list what looks like three different companies — but it's actually one. You figure out how to resolve that automatically across millions of records
• Given raw people data, infer the org chart — who reports to whom, what the team structure looks like, how the engineering org differs from sales
• Detect what technologies a company uses from unstructured signals scattered across the web
• Classify whether a job change was a promotion, lateral move, demotion, or just a title edit — and do it for millions of transitions
• Map raw job titles to canonical titles, seniority levels, and job functions — across dozens of languages and naming conventions

Nice to haves

• Published research or conference papers (NeurIPS, ICML, ICLR, ACL, EMNLP, etc.)
• Experience with entity resolution or record linkage at scale
• Built taxonomy or ontology systems over messy real-world data
• Background in multilingual NLP or cross-lingual transfer
• Open-source contributions in NLP/IR
• Experience with distributed training on GPU clusters

Compensation & perks

• $8,000–$14,000/month (above market rate for SF internships)
• Housing stipend for those relocating to SF
• Direct mentorship from the founding team — no layers between you and the CEO
• Your work ships to production and reaches real customers