Job description

About Ancestry: When you join Ancestry, you join a human-centered company where every person’s story is important. Ancestry®, the global leader in family history, connects everyone with their past so they can discover, preserve, and share their unique family stories. With our unparalleled collection of more than 65 billion records, over 3.5 million subscribers, and over 27 million people in our growing DNA network, customers can discover their family story and gain a new level of understanding about their lives. Over the past 40 years, we’ve built trusted relationships with millions of people who have chosen us as the platform for discovering, preserving, and sharing the most important information about themselves and their families. 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. Additional Information: Ancestry is an Equal Opportunity Employer that makes employment decisions without regard to race, color, religious creed, national origin, ancestry, sex, pregnancy, sexual orientation, gender, gender identity, gender expression, age, mental or physical disability, medical condition, military or veteran status, citizenship, marital status, genetic information, or any other characteristic protected by applicable law. In addition, Ancestry will provide reasonable accommodations for qualified individuals with disabilities. All job offers are contingent on a background check screen that complies with applicable law. For candidates who live in San Francisco, CA, pursuant to the San Francisco Fair Chance Ordinance, Ancestry will consider for employment qualified applicants with arrest and conviction records. Ancestry is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at Ancestry via-email, the Internet or in any form and/or method without a valid written search agreement in place for this position will be deemed the sole property of Ancestry. No fee will be paid in the event the candidate is hired by Ancestry as a result of the referral or through other means.

Requirements

Must-haves

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.
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

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningpythonmlllmainode.jsjava

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Effort: medium