MTS - Staff Engineer

Collinear AI; Sunnyvale, Northern (CA, KY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Collinear AI is seeking a Member of Technical Staff (MTS - Research) to bridge frontier research and production engineering in Sunnyvale, CA. You will develop high-fidelity environments and evaluation stacks for stress-testing agents against real-world metrics.

You will iterate on novel RL approaches and translate them into scalable infrastructure, collaborating daily with the founders and research staff to advance AI reliability and impact.

Qualifications

• A Bachelor’s, Master’s, or PhD in a technical field (CS, Math, Physics, etc.) or demonstrated open-source contributions or industry experience.
• A strong foundation in software engineering with the ability to build robust, scalable infrastructure.
• A principled understanding of foundation models, including how they are constructed, evaluated, and optimized.
• Experience conducting research or technical experiments with a focus on reproducibility and data-driven results.

Responsibilities

• Build Agentic Environments: Design and implement the next generation of SimLabs, ultra-realistic, long-horizon simulation environments.
• Programmatic Verification: Develop rigorous, policy-aware judges and evaluations that measure genuine capability and safety.
• Close the Loop: Design and execute high-quality post-training runs (CPT, SFT, RL) for frontier performance on open-source models.
• Rapid Iteration: Debug and iterate across the ML stack, from infrastructure to model behavior, CLI-first.
• Collaborate: Work daily with the founders and research staff to shape the roadmap and push state-of-the-art in AI reliability.

Skills

Python

CLI-first development

Reinforcement Learning

Research mindset

Education

Bachelor’s, Master’s, or PhD in CS/Math/Physics or related field

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.