Machine Learning Engineer, Co-op
Remote, United States
Ancestry
Curious about careers at Ancestry? Explore our culture, career areas and search opportunities.About Ancestry:
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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 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.
What You Will Do:
Develop and deploy machine learning models, including 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 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 go-getter attitude
Experience with cloud platforms, ML development tools, and ML deployment tools.
Nice to have: Familiarity NodeJS or Java
Nice to have: Familiarity with LLM fine-tuning, retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, OpenSearch), LLM optimization, VLLM library, HuggingFace library or reinforcement learning techniques.
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 San Francisco office candidates, 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.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Computer Science Engineering FAISS Generative AI GPT HuggingFace Java LangChain Linguistics LLaMA LLMs Machine Learning Mathematics ML models MLOps Model inference Model training Node.js OpenSearch PhD Pinecone Pipelines Python PyTorch RAG Reinforcement Learning Scikit-learn Statistics TensorFlow vLLM
Perks/benefits: Flex hours
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