Machine Learning Engineer, Co-op

Ancestry

2d • Remote

About The Position

Ancestry seeks an exceptional, passionate, and highly motivated Machine Learning Engineer Co-Op to join our MLE team this summer. The MLE team is responsible for developing, deploying, fine-tuning and optimizing machine learning models and LLMs to enhance customer experiences, improve internal workflows, and drive business impact. We collaborate closely with data scientists, engineers, and product teams to build scalable and efficient ML solutions that power critical features across our platform. As a Machine Learning Engineer Co-Op on the MLE team, you will work on integrating ML models and Generative AI (GenAI) models, enabling ML/LLM-powered applications, and developing AI agents using agentic frameworks. You will contribute to optimizing model inference, automating ML workflows, and building intelligent AI-driven solutions to improve decision-making and user engagement. This is a part-time, work-study-based opportunity for active students in master's and PhD programs.

Requirements

• Currently pursuing an advanced degree (Master's or PhD preferred) in Computer Science, Data Science, Statistics, Mathematics, Linguistics, Engineering or related quantitative field with a strong data focus.
• Proficient in Python and familiar with ML libraries such as TensorFlow, PyTorch or Scikit-learn.
• Experience with GenAI, LLMs, and agentic frameworks (LangChain, AutoGen).
• Strong problem-solving skills, with the ability to write clean, efficient, and scalable code.
• Strong written and verbal communication skills
• Curiosity and go-getter attitude
• Experience with cloud platforms, ML development tools, and ML deployment tools.

Nice To Haves

• Familiarity NodeJS or Java
• Familiarity with LLM fine-tuning, retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, OpenSearch), LLM optimization, VLLM library, HuggingFace library or reinforcement learning techniques.

Responsibilities

• Develop and deploy machine learning and large language models.
• Build and optimize AI agents to enhance automation and decision-making.
• Optimize model inference speed, storage efficiency, and scalability for real-world applications.
• Develop pipelines and MLOps workflows to streamline model training, evaluation, and deployment.
• Contribute to ML, LLMs, agent evaluation and monitoring platform.
• Experiment with new ML, LLM, and Agent technologies.

What This Job Offers

• Job Type: Part-time
• Career Level: Intern