Ph.D.  Intern – Machine Learning & Generative AI

Remote

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Not SpecifiedCompensation

InternshipExperience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, Legal Tech, Generative AIIndustries

Requirements

Candidates must be pursuing a Ph.D. in Computer Science, Electrical and Computer Engineering, Statistics, or a related field with a research focus in Machine Learning, Natural Language Processing, or Generative AI. A strong understanding of modern LLM architectures, training/fine-tuning pipelines, and evaluation methods is required, along with fluency in Python. Demonstrated ability to translate research concepts into functional prototypes and a high degree of intellectual curiosity, rapid learning capability, and resilience in ambiguous, early-stage environments are essential. Experience with agent frameworks, long-context or multimodal models, vector databases, RAG orchestration, or document triage pipelines is considered a plus.

Responsibilities

The Ph.D. Intern will research and prototype novel approaches in areas such as retrieval-augmented generation, tool-use agents, multimodal LLMs, or self-supervised document representation. Responsibilities include adapting and fine-tuning models for specialized medical and legal corpora, building and benchmarking agentic systems for tasks like OCR and precedent citation, and developing evaluation suites to measure factuality, legal soundness, and bias, proposing mitigations where necessary. The intern will also contribute to advancing the field through potential conference submissions and will see their code integrated into the product.

Skills

Machine Learning

Generative AI

Large Language Models (LLMs)

Retrieval-Augmented Generation

Tool-Use Agents

Multimodal LLMs

Self-Supervised Learning

Document Representation

Fine-tuning

Agentic Systems

OCR

Factuality Evaluation

Bias Mitigation

Python

Research

Prototyping

EvenUp

Legal services for personal injury cases

About EvenUp

EvenUp Law provides legal services focused on personal injury, trucking accidents, and medical malpractice. The firm operates in multiple states, including Texas, Pennsylvania, California, Indiana, and Georgia. Its business model is based on a contingency fee structure, meaning they only receive payment if they win a case, taking a percentage of the settlement or judgment. What sets EvenUp Law apart from its competitors is its detailed case preparation and the ability to analyze cases like an adjuster or defense attorney, which leads to higher settlement amounts and quicker resolutions for clients. The firm also maintains a database of similar injuries and their values, which helps in providing accurate damage estimates. The goal of EvenUp Law is to save clients time and stress while ensuring they receive fair compensation for their injuries.

San Francisco, CaliforniaHeadquarters

2019Year Founded

$214MTotal Funding

SERIES_DCompany Stage

LegalIndustries

201-500Employees

Risks

Increased competition from other legal tech startups could erode EvenUp's market share.

Reliance on a contingency fee model poses financial risks with unsuccessful cases.

Potential regulatory changes in AI use could impact EvenUp's operations.

Differentiation

EvenUp uses AI to automate legal document creation, reducing time and costs.

The company targets the $20 million insurance claim market with AI-driven solutions.

EvenUp's meticulous case preparation results in high settlement amounts and quick resolutions.

Upsides

Recent $135 million Series D funding shows strong investor confidence in EvenUp.

Launch of AI assistant Litty highlights growth in generative AI for legal automation.

Strategic board appointments could open new avenues for growth and partnerships.

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