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
Scale works with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling). This role will focus on optimizing data curation and eval to enhance LLM capabilities in both text and multimodal modalities.
In this role, you will develop novel methods to improve the alignment and generalization of large-scale generative models. You will collaborate with researchers and engineers to define best practices in data-driven AI development. You will also partner with top foundation model labs to provide both technical and strategic input on the development of the next generation of generative AI models.
Key Responsibilities & Day-to-Day Scope
- ✓ Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities.
- ✓ Design and experiment new approaches to preference optimization.
- ✓ Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness.
- ✓ Publish research findings in top-tier AI conferences.
- ✓ Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field.
Official Qualifications & Technical Requirements
- • Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.
- • Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning.
- • Excellent written and verbal communication skills
- • Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals
- • Previous experience in a customer facing role.
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.