About the role: As a Machine Learning Fellow, you will apply machine learning techniques to high-impact research problems. Fellows will contribute across the ML lifecycle — from curating and processing diverse data sources, to developing, fine-tuning, and evaluating models. This is a hands-on role at the intersection of applied research and practical ML development, with opportunities to explore novel methods, test ideas quickly, and generate insights. 

In this role, you will:

• Assist in the development and maintenance of bespoke classification systems, contributing across the project lifecycle.
• Collaborate with engineers across disciplines to co-design project timelines for model development, gaining hands-on experience in planning data pipelines, building infrastructure, deploying models, and assessing performance/latency metrics.
• Research new approaches to help streamline existing processes with the help of machine learning algorithms.
• Assist with research experiment design and automation, particularly as it relates to abuse detection or red teaming of AI systems.
• Ideate / brainstorm new research approaches to known and novel problems in the Trust & Safety and AI Security fields.

We’re looking for someone who: 

• Brings curiosity and creativity to ambiguous research problems, with a bias toward experimentation and rapid iteration. 
• Thrives in collaborative, interdisciplinary environments; is resourceful, proactive, and adaptable.
• Is comfortable communicating technical ideas clearly to both technical and non-technical audiences. 
• Is excited about contributing to real-world applications of ML and exploring new methods that push beyond standard benchmarks. 

Requirements:

• Strong academic background and quantitative foundation demonstrated through applied machine learning coursework, research, or hands-on-experience.
• Experience with NLP foundations and text data processing, including cleaning, tokenization, and feature engineering for downstream model development.
• Strong Python background with practical experience using multiple ML frameworks (PyTorch, TensorFlow, scikit-learn, etc.) to prototype, train, and evaluate models in real-world applications.
• Practical experience in generative model adaptation through fine-tuning, prompt-engineering, and in-context learning on low-resource or specialized datasets
• Clear communicator of technical concepts for non-technical audiences.
• Strong understanding of modeling concepts including bias-variance tradeoffs, regularization, generalization, data imbalance, and model calibration to design models in challenging problem spaces.

Nice to have:

• Computer vision skills (OCR, image classification, deep fake detection).
• Experience working in cloud environments such as AWS or GCP for end-to-end ML workflows, including model training, deployment, and monitoring using tools like Vertex AI, SageMaker, and cloud-native ML libraries.
• Familiarity with multimodal learning (text-image or text-audio) or cross-domain model evaluation.
• Exposure to MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.).
• Experience managing full lifecycle machine learning projects from design to deployment.
• Understanding of modern retrieval-augmented generation (RAG), AI agent frameworks, and context-aware orchestration (e.g., LangChain, LlamaIndex, OpenAI Agents, or AutoGen) for building intelligent applications.

Benefits:

• Flexible start / end dates
• Remote work (based in the continental U.S.) 
• Flexible schedule, up to 20 hours per week (negotiable)
• Hourly pay commensurate with experience and qualifications
• $30 per hour for undergraduate students 
• $35 per hour for graduate students
• $50 per hour for advanced PhD students
• $60 per hour for postdocs or non-tenured positions
• $125 per hour for tenure-track academics