MTS - Research (India)

Collinear AI, Inc.; India; On-site; INR 700,000 - 1,100,000; Full time

Job summary

Collinear AI, Inc. is seeking a Member of Technical Staff (Research) to bridge frontier research and production engineering for data engines powering frontier AI.

You will build ultra-realistic simulation environments and evaluation stacks used by leading labs to stress-test advanced agents. You will iterate on novel RL approaches, translating them into scalable infrastructure that improves real-world model metrics, while collaborating closely with founders and research staff to shape the

Qualifications

• Bachelor’s, Master’s, or PhD in a technical field or demonstrated proof of work through OSS contributions or industry experience.
• Strong software engineering foundations and ability to build robust, scalable infrastructure.
• Principled understanding of foundation models, evaluation, and optimization.
• Experience in research or technical experiments with emphasis on reproducibility and data-driven results.

Responsibilities

• Build Agentic Environments: design and implement ultra-realistic, long-horizon simulation environments for agent learning.
• Programmatic Verification: develop policy-aware judges and evaluations to measure capability and safety.
• Close the Loop: design and execute post-training runs to deliver frontier performance on open-source models.
• Rapid Iteration: debug and iterate across the ML stack from infrastructure to model behavior.
• Collaborate: work daily with founders and research staff to shape the roadmap and push state-of-the-art.

Skills

Python

CLI-first

Research experience

Reinforcement Learning

Software engineering

Education

Bachelor’s/Master’s/PhD in CS/Math/Physics

Tools

Open-source contributions

Simulation systems

Job description

About the Role

We are looking for a Member of Technical Staff (MTS - Research) 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 strong 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 constructed, evaluated, and optimized.

• Empirical Mindset: Experience conducting research or technical experiments with a focus on reproducibility and data-driven results.

What will make you stand out

• Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.

• Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.

• Domain Expertise: Prior experience with Reinforcement Learning (RLHF/RLAIF), simulation systems, or building long-horizon agentic environments.

• Proven Track Record: A history of contributing to influential ML research (e.g., publications at NeurIPS, ICLR, ICML) or maintaining high-impact open-source projects.

• Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver "frontier performance" on open-source benchmarks.