Machine Learning Engineer, Co-op

Ancestry

31d

About The Position

Ancestry seeks a passionate and highly motivated Machine Learning Engineer Co-Op to join its MLE team. This engineering-first role involves writing production-quality Python code and independently building, testing, and debugging ML-powered software. The co-op will work on model integration and deployment, ML/LLM applications, AI agents, and scalable ML infrastructure, translating machine-learning capabilities into reliable production systems.

This is a part-time, work-study-based opportunity for active students in Master's and PhD programs. Ancestry uses a location-flexible work approach, with options to work from a nearby office, from home, or in a hybrid arrangement, subject to location restrictions and role requirements.

Requirements

• 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 Haves

• Experience with Node.js or Java.
• Experience with LLM fine-tuning, RAG, vector databases, Hugging Face, vLLM, inference optimization, or reinforcement learning.

Responsibilities

• 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.

What This Job Offers

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