Collinear is an early-stage, seed-backed startup helping enterprises deploy AI they can trust.
Our platform simulates realistic, multi-turn user journeys to stress-test agents. It analyzes behavior with policy-aware judges across 20+ performance, safety, and reliability metrics. It then curates high-signal, semi-synthetic data for post-training (CPT/SFT/RL).
The closed loop—simulate → analyze → improve—turns evaluation gaps into measurable quality lift in production.Why Join Collinear
Own meaningful surface area: design and ship features across simulations, judges, and post-training data pipelines.
See impact quickly: small team, short iteration cycles, production users.
Work at the core of LLM quality: multi-turn agent sims, metric/benchmark design, CPT/SFT/RL data curation.
Direct access to decision-makers: collaborate daily with founders on roadmap and results.
Solid footing for speed: seed-backed, active customer pilots, and room to shape the direction.
Join us in pushing the boundaries of what's possible in AI! Learn more about the company here.
About the Role
As a Research Scientist Intern, you will support our technical team in developing and improving our core product pipeline. This internship offers a unique opportunity to contribute to cutting-edge research in AI safety and reliability while gaining valuable industry experience. You'll work alongside experienced researchers and engineers to help translate research into practical applications.
Key Responsibilities
Assist in implementing and testing machine learning models and systems
Write blogposts
Open source datasets and models
Write research papers at top conferences (COLM, ICLR, NeurIPS)
Support research activities related to foundation models and AI safety
Help optimize ML performance and contribute to distributed training efforts
Participate in data curation and analysis for model improvement
Collaborate with the technical team on research projects
About You
Currently pursuing a Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field
Strong foundation in machine learning concepts and techniques
Experience with PyTorch, TensorFlow, or similar ML frameworks
Solid programming skills, particularly in Python
Basic understanding of foundation models and their applications
Eagerness to learn about AI safety and reliability
Strong problem-solving abilities and attention to detail
Excellent communication skills and ability to work in a team environment
What We Offer
Hands-on experience with cutting-edge AI research and development
Mentorship from industry experts
Exposure to real-world applications of foundation models
Collaborative and innovative work environment
Opportunity to contribute to projects with significant impact
Duration
3-6 months, with potential for extension or conversion to full-time based on performance
This will be a paid position in line with market competitive rates!
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