Role Description & Responsibilities
Machine Learning
About Scale AI
Scale AI is a high-growth technology leader building scalable solutions for global users. Millions of companies and teams rely on their infrastructure and services to execute high-value workflows, analyze massive datasets, and scale modern operations.
CadetX Candidate Advantage: As an applicant for this Machine Learning role, you are evaluated on practical proficiency with real-world query execution, data pipelines, and clear stakeholder communication.
CadetX Role Brief & Company Specifications
Role Mission & Impact
At Scale , our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force.
Scale's Machine Learning Research team builds the foundation for the next generation of AI systems — pushing the boundaries of what's possible with frontier models while ensuring safety, reliability, and alignment at every step. Our work spans generative AI, reinforcement learning and advanced post-training methods, scalable oversight, synthetic data pipelines, red teaming, and evaluation science.
Key Responsibilities & Day-to-Day Scope
- ✓ Measure the dangerous capabilities of frontier models and conduct preparedness research
- ✓ Research the science and creation of new benchmarks for frontier models
- ✓ Develop new methods for training models to excel on extremely difficult reasoning problems that require long chains of thought
- ✓ Research reinforcement learning and post-training methods that improve model reliability on high-stakes tasks
- ✓ Research scalable oversight protocols that enable humans to produce and quality control reasoning chains beyond their native capabilities
- ✓ Study the boundaries of model generalization and capabilities to inform data-driven advancements
- ✓ Research synthetic and hybrid data with humans in the loop to scale up high-quality data generation
- ✓ Take models currently in production, identify areas for improvement, improve them using retraining and hyperparameter searches, then deploy without regressing on core model characteristics
- ✓ Create post-training or agentic solutions that integrate into our ability to deliver applications for enterprise and public sector clients
- ✓ Currently enrolled in a BS, MS or PhD program with a focus on Machine Learning, Deep Learning, or Natural Language Processing, with a graduation date in Fall 2027 or Spring 2028
- ✓ Available for a Summer 2027 internship (May/June start dates) in San Francisco Or New York.
- ✓ Prior research experience or a track record of publications on LLMs, NLP, multimodal models, agents, safety, evaluation, alignment, or a related field
Official Qualifications & Technical Requirements
- • Experience with one or more general purpose programming languages, including Python, Javascript, or similar
- • A previous internship in Machine Learning, Deep Learning, Natural Language Processing, Adversarial Robustness, Alignment, Evaluation, or Agents
- • Experience as a researcher — through internships, full-time roles, or at a lab
- • Publications at top-tier venues such as NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP or AAAI, or contributions to open source projects
- • Hands-on experience with reinforcement learning, agentic systems, or evaluation infrastructure
- • Earn Customer Love — trust is earned with every interaction, every delivery, and every problem we solve
- • Team Flow — ideas, energy, and support move freely across teams; optimize for the whole
- • Quality is Our Cheat Code — the systems that deliver quality consistently are a structural advantage
- • Find the 20% — find the inputs that drive the outcomes and execute them completely
- • Write the Market — our research puts us at the frontier, and we bring our partners there first
- • Three Moves Ahead — consider the consequences of consequences, and plan accordingly
Preferred Skills & Technology Stack
- + Familiarity with MLOps pipelines (Docker, Kubernetes, Kubeflow, Triton Inference Server).
- + Knowledge of distributed training techniques and GPU acceleration (CUDA).
- + Relevant coursework, research publication, or open-source contribution in ML/DL.
Tech Stack & Tools in this role
Expected technical competencies for Day 1 productivity:
★ Life & Culture at Scale AI
Verified EmployerScale AI champions rigorous analytical discipline, continuous learning, and low-ego teamwork. Team members enjoy flexible hybrid allowances, learning stipends, and structured mentorship to fast-track career mobility.