Member of Technical Staff

About Collinear

At Collinear, we help teams fearlessly ship AI. Frontier labs and AI-native companies use our SimLab to find capability gaps in their agents and generate high-quality data to close them. We believe that the next generation of AI progress won't come from just bigger models, but from more rigorous, long-horizon simulation and programmatic verification. SimLab allows researchers to spin up realistic environments, run agents through complex tasks, and surface failure modes under real-world conditions. We then close the loop by generating targeted synthetic data to retrain models, delivering measurable quality lift on the metrics that actually matter.

About the Role

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.

Why Join Collinear

• Own the Frontier: Work on the most pressing problem in AI today: making agents reliable enough for production.
• High Density of Talent: Join a small, elite team where you will be pushed to do your life's work.
• Elite Compensation: We offer competitive salary and equity packages to ensure we attract the best of the best.

Requirements

• 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.

Qualifications

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.
• 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.