ML Engineering Co-op: Build AI Agents & LLMs

Ancestry; United States; Hybrid; USD 22,000 - 33,000; Part time

Benefits offered by this job

Paid internship stipend

Flexible work location

Job summary

Ancestry is seeking a highly motivated Machine Learning Engineer Co-Op to join our MLE team this summer. You will collaborate with data scientists, engineers, and product teams to build scalable ML solutions that power our platform.

You will work on developing, deploying, and fine-tuning ML models and LLMs, while contributing to AI agents and monitoring platforms. A Master’s or PhD candidate in a quantitative field is preferred, with strong Python and ML library experience.

Qualifications

• Pursuing an advanced degree (Master's or PhD preferred) in CS, Data Science, or related field with strong data focus.
• Proficient in Python and ML libraries; experience with GenAI/LLMs is preferred.
• Strong problem-solving, communication skills and willingness to learn new technologies.

Responsibilities

• Develop and deploy machine learning models and large language models.
• Build and optimize AI agents to enhance automation and decision-making.
• Improve inference speed, storage efficiency, and scalability for real-world apps.
• Create pipelines and MLOps workflows for training, evaluation and deployment.
• Contribute to ML/LLM/agent evaluation and monitoring platforms.
• Experiment with new ML/LLM/agent technologies.

Skills

Python

GenAI

LLMs

Problem solving

Communication

Curiosity

Cloud platforms

Education

Master's or PhD in a quantitative field

Tools

TensorFlow

PyTorch

Scikit-learn

LangChain

AutoGen

FAISS

Pinecone

OpenSearch

HuggingFace

VLLM

Job description

Ancestry is seeking a highly motivated Machine Learning Engineer Co-Op to join our MLE team this summer. You will collaborate with data scientists, engineers, and product teams to build scalable ML solutions that power our platform.

You will work on developing, deploying, and fine-tuning ML models and LLMs, while contributing to AI agents and monitoring platforms. A Master’s or PhD candidate in a quantitative field is preferred, with strong Python and ML library experience.