Machine Learning Engineer, Co-op

We are committed to our location flexible work approach, allowing you to choose to work in the nearest office, from your home, or a hybrid of both (subject to location restrictions and roles that are required to be in the office- see the full list of eligible US locations HERE). We will continue to hire and promote beyond the boundaries of our office locations, to enable broadened possibilities for employee diversity.

Together, we work every day to foster a work environment that's inclusive as well as diverse, and where our people can be themselves. Every idea and perspective is valued so that our products and services reflect the global and diverse clients we serve.

Ancestry encourages applications from minorities, women, the disabled, protected veterans and all other qualified applicants. Passionate about dedicating your work to enriching people’s lives? Join the curious.

Ancestry seeks an exceptional, passionate, and highly motivated Machine Learning Engineer Co-Op to join our MLE team. As a Machine Learning Engineer Co-Op, you will be expected to write production-quality Python code and independently build, test, and debug ML-powered software. You will work on model integration and deployment, ML/LLM applications, AI agents, and scalable ML infrastructure. This is an engineering-first role that requires strong software development fundamentals and the ability to translate ML capabilities into reliable production systems.

This is a part-time, work-study-based opportunity for active students in Master's and PhD programs.

What You Will Do:

Design, build, test, and deploy production-quality ML software in Python.
Build scalable services, pipelines, and APIs that integrate ML models and LLMs.
Develop and optimize AI agents and agentic applications.
Optimize model inference for performance, reliability, scalability, and cost.
Build MLOps workflows for model training, evaluation, deployment, and monitoring.
Debug complex technical issues and independently own engineering deliverables.
Evaluate and implement emerging ML, LLM, and agent technologies.

Who You Are:

Currently pursuing a Master's or PhD in Computer Science, Software Engineering, Computer Engineering, Machine Learning, AI, or a related technical field.
Highly proficient in Python with demonstrated ability to independently build, test, and debug software.
Strong software engineering fundamentals, including data structures, algorithms, APIs, testing, version control, and code quality.
Experience with ML libraries such as PyTorch, TensorFlow, or Scikit-learn.
Experience building with GenAI, LLMs, and agentic frameworks such as LangChain, LangGraph, or AutoGen.
Experience with cloud platforms, ML development tools, and production deployment workflows.
Strong problem-solving and technical communication skills.
Nice to have: Experience with Node.js or Java; experience with LLM fine-tuning, RAG, vector databases, Hugging Face, vLLM, inference optimization, or reinforcement learning.