AI​/ML Scientist Intern, AIMS AI Foundations; PhD – Fall

The AIMS AI Foundations team sits within the AI for Member Systems organization and focuses on the research and engineering foundations that underpin next‑generation member experiences. Our work spans agentic AI systems, LLM evaluation frameworks, multimodal modeling, and training data curation.

We are seeking a PhD intern for a Fall
2026 engagement, targeting a September
2026 start date. This is a hands‑on applied research role: you will design and run experiments, build prototypes, and contribute to ongoing team projects in one or more of our core domain areas.

• Agentic AI – Developing and evaluating systems that reason, plan, and act autonomously, including tool use, retrieval‑augmented reasoning, memory and goal management, and feedback‑driven learning.
• LLM Evaluations – Designing rigorous evaluation frameworks, benchmarks, and quality metrics to assess language model behavior, reliability, and alignment.
• Multimodal Data – Building models and pipelines that integrate text, image, video, audio, and other data modalities; experience with large vision‑language models and modality fusion.
• LLM Training Data Curation – Researching and implementing methods for selecting, filtering, and improving training data quality to enhance model performance.

• Currently enrolled PhD student in Computer Science, Machine Learning, Artificial Intelligence, Computer Engineering, Mathematics, Statistics, Data Science, Cognitive Science, or a related field.
• Able to work 40 hours per week during the fall/winter.
• Proficiency in Python.
• Strong foundation in machine learning, deep learning, and algorithms/statistics.
• Experience with one or more major ML frameworks:
  PyTorch, Tensor Flow, or JAX.
• Ability to design, run, and interpret ML experiments in an applied research setting.
• Ability to translate research ideas into practical prototypes and evaluations.
• Strong oral and written communication skills for presenting technical work clearly.

• Coursework or research experience in advanced NLP, advanced ML systems, or reinforcement learning.
• Familiarity with Hugging Face, Transformers, Pandas, Num Py, and scikit‑learn.
• Publications in top venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, AAAI, CIKM,(Use the "Apply for this Job" box below). UAI, CVPR, or related.
• Experience with agentic systems, multimodal modeling, or applied LLM workflows.
• Exposure to evaluation design, benchmarking, and model quality tradeoffs.
• Familiarity with distributed computing environments such as Spark or Presto.
• Comfortable with software engineering best practices (version control, testing, code review).

• Duration: minimum 12 weeks, targeting a September
  2026 start date.
• Location:
  
  Los Gatos, CA headquarters or remote; flexible depending on the team.
• This program is intended for students who will be returning to school for at least one semester/quarter following the internship.

Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family‑forming benefits, and Life and Serious Injury Benefits. Paid leave of absence programs are also available. Full‑time hourly employees accrue 35 days annually for paid time off. Full‑time salaried employees are immediately entitled to flexible time off.

Netflix is an equal‑opportunity employer and celebrates diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.