Machine Learning Research Intern

W orlds builds operational AI for the physical world. Our platform connects real-time camera feeds to production-grade computer vision models, enabling clients across logistics, aviation, and industrial operations to automate decisions  train custom detection, tracking, and search models on client-specific environments and deploy them live across multi-camera setups on cloud infrastructure.

*** YOU MUST BE A TEXAS BASED RESIDENT TO APPLY FOR THIS ROLE***

We are looking for a motivated ML Research Intern to join our team for Summer 2026 (3 months, remote). Working closely with our forward research team, you will help explore and shape emerging research directions that will define the next evolution of our platform.

These are forward-looking areas — part of your role will be to help scope what is worth pursuing and how it connects to real operational problems we see across our clients.

Programming Proficiency:
Strong experience in programming languages such as Python, Java, or Java Script

Research how behavioral patterns of people, vehicles, and assets can be modeled over time to enable richer search, retrieval, and anomaly detection — for example, understanding what is normal for a given environment and surfacing meaningful deviations.

Investigate how relationships between objects, events, and environments can be represented as graph structures to enable more expressive querying and inference across complex operational scenes.

Develop approaches to detect, classify, and interpret behavioral signals from continuous video streams — moving from object-level detection toward understanding what is actually happening in a scene over time.

Explore and synthesize cutting-edge research across your area of interest and assess applicability to real-world operational AI

Collaborate with the research and engineering team to understand problem space and contribute meaningful research thinking

Develop and test hypotheses, run experiments, and iterate on ideas in a fast-moving environment

Communicate findings clearly through written summaries and discussions with the team

Help define what directions are worth pursuing and contribute to shaping the research roadmap

Currently pursuing an MS or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field

Solid foundations in machine learning — comfortable with training, evaluation, and debugging models in code, not just theory

Hands-on experience with LLM fine-tuning or working with large pre-trained models (e.g. Mistral, Gemma, LLaMA, or similar)

Strong Python skills — you write clean, reproducible ML code, not just research scripts

Familiarity with statistical thinking around model evaluation — confidence intervals, significance, metric design

Comfortable working with ambiguity and defining problem structure, not just executing defined tasks

Experience with computer vision models or detection pipelines (YOLO, RT-DETR, or similar)

Familiarity with semantic search, embeddings, or retrieval systems (CLIP, Qdrant, FAISS)

Exposure to MLOps tooling (MLflow, ZenML, Metaflow, or similar)

Experience with VLMs (LLaVA, MoLMo, Gemma Vision, or similar)

Prior industry internship experience in an applied ML or research engineering role