Machine Learning Engineer Co-Op
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Job Description
About Ancestry:
Ancestry is the global leader in family history, empowering journeys of personal discovery to enrich lives. With over 40 billion records, over 3 million subscribers, and over 23 million people in its DNA network, Ancestry helps customers discover their family stories. The company is committed to a location-flexible work approach, offering options to work in an office, from home, or a hybrid of both. Ancestry fosters an inclusive and diverse work environment where every idea and perspective is valued.
Job Description: Machine Learning Engineer Co-Op
Ancestry seeks a highly motivated Machine Learning Engineer Co-Op to join its MLE team for the summer. The MLE team develops, deploys, fine-tunes, and optimizes machine learning models to enhance customer experiences, improve internal workflows, and drive business impact. The team collaborates closely with data scientists, engineers, and product teams.
What You Will Do:
• Develop and deploy machine learning models using TensorFlow, PyTorch, GenAI, and LLM-based applications, working closely with data scientists and engineers.
• Build and optimize AI agents using agentic frameworks 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.
• Experiment with new ML & LLM technologies, vector databases, and retrieval-augmented generation (RAG) techniques, LLM optimization, and more.
Who You Are:
• Currently pursuing an advanced degree (Master’s or PhD preferred) in Computer Science, Data Science, Statistics, Mathematics, Linguistics, Engineering, or a 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 (GPT, LLaMA, Mistral, Phi), 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 a “go-getter” attitude.
• Experience with cloud platforms, ML development tools, and ML deployment tools.
Nice to have:
• Familiarity with 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.
