Staff Machine Learning Engineer (Systems) at EvenUp

San Francisco, California, United States

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$215,000 – $323,000Compensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Legal, SoftwareIndustries

Requirements

Candidates should possess strong software engineering skills, including proficiency in Python, distributed computing, and API development. A deep understanding of transformer models, such as LLMs, embeddings, and fine-tuning methods like LoRA and PEFT, is required. Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus, Elasticsearch/OpenSearch) and retrieval frameworks (LangChain, LlamaIndex, custom retrieval pipelines) is preferred, as is knowledge of evaluation methodologies for generative AI, including RAG benchmarks and hallucination reduction.

Responsibilities

The Staff Machine Learning Engineer will lead the design and architecture of large-scale ML systems for RAG, vector search, and fine-tuning frameworks. They will define and drive technical strategy and best practices for ML system design, including embedding pipelines and evaluation frameworks. Responsibilities include mentoring other engineers, partnering with cross-functional teams to translate business goals into ML system architectures, and driving innovation in areas like semantic search and generative AI evaluation. The engineer will own ML workflows for reproducibility and scalability, establish system evaluation standards, and act as a bridge between ML research and engineering for production integration.

Skills

Machine Learning

Natural Language Processing

Generative AI

Data Quality

Model Development

System Design

Embedding Pipelines

Vector Databases

RAG

Vector Search

Fine-tuning

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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