We are looking for Research Scientists and Research Engineers to help us build the data engine for frontier AI. In this role, you will bridge the gap between frontier research and production engineering. You will develop the high-fidelity environments and evaluation stacks that the world’s leading AI labs rely on to stress-test their most advanced agents. Your work will involve iterating on novel RL approaches and translating them into robust, scalable infrastructure that moves the needle on real-world model metrics.
Responsibilities:
Build Agentic Environments: Design and implement the next generation of "SimLabs", ultra-realistic, long-horizon simulation environments where agents learn to navigate ambiguity and maintain context.
Programmatic Verification: Develop rigorous, policy-aware judges and evaluations that measure genuine capability and safety beyond simple benchmarks.
Close the Loop: Design and execute high-quality post-training runs (CPT, SFT, RL) to deliver frontier performance on open-source models using curated, high-signal data.
Rapid Iteration:
Debug and iterate across the full ML stack, from infrastructure to model behavior, ensuring our tools remain "command-line first" and developer-friendly.
Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.
About You
We are looking for individuals who demonstrate a rare combination of technical depth, research intuition, and high agency.
Technical Foundation: A Bachelor’s, Master’s, or PhD in a technical field (CS, Math, Physics, etc.), or a demonstrated "proof of work" through significant open-source contributions or industry experience.
Engineering Rigor: A solid foundation in software engineering with the ability to build robust, scalable infrastructure. You should be comfortable in a Python-friendly, CLI-first development environment.
ML Fluency: A principled understanding of foundation models, including how they are construct
📌 MTS - Research (India)
🏢 Collinear AI
📍 India
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