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10a Labs · 3 months ago

Machine Learning Fellowship

New York, NY

Internship

Onsite

Intern

$30/hr - $125/hr

10a Labs is an applied research and AI security company trusted by AI unicorns and Fortune 10 companies. As a Machine Learning Fellow, you will apply machine learning techniques to high-impact research problems, contributing across the ML lifecycle from data processing to model evaluation.

Research

Responsibilities

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

Qualification

Machine LearningPythonNLPGenerative ModelsCloud EnvironmentsMLOpsCommunicationCollaborationCreativity

Required

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

Preferred

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