Member of Technical Staff

Collinear AI; Sunnyvale (CA); On-site; USD 170,000 - 210,000; Full time

Benefits offered by this job

Competitive salary and equity packages

Health insurance

Job summary

Collinear AI in Sunnyvale, CA is seeking an MTS - Research to help build the data engine for frontier AI. You will design ultra-realistic SimLabs and evaluation stacks that stress-test agents across domains including simulation and toolcalling.

You bring a technical foundation, strong software engineering, and ML fluency with a focus on reproducibility and data-driven results. Join a small, elite team and impact the reliability of next-generation AI systems.

Qualifications

• Bachelor’s/Master’s/PhD in CS/Math/Physics or demonstrated equivalent open-source/industry work.
• Strong software engineering fundamentals with the ability to build robust, scalable infrastructure in a Python CLI-first environment.
• Principled understanding of foundation models, including construction, evaluation, and optimization.
• Experience conducting research with reproducibility and data-driven results.

Responsibilities

• Build Agentic Environments: design ultra-realistic simulators and long-horizon environments.
• Programmatic and Agentic Verification: develop policy-aware judges and evaluations for capability and safety.
• Close the Loop: run post-training CPT, SFT, RL on open-source models using curated, high-signal data.
• Collaborate with founders and researchers to shape roadmap and advance reliability.

Skills

Python

Reinforcement Learning

Research mindset

Data-driven experiments

Education

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

Tools

Git

Open-source contributions

Job description

We are looking for MTS - Research team member to help us build the data engine for frontier AI. 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. You will work across domains including Computer Use, Enterprise MCP/Toolcalling and Coding. Verifier Design, Simulated Personas, Benchmarking Personal AGI are some of the research areas we work on.

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 and Agentic 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.
• 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.

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.
• Direct Impact: At a Series A startup, your work directly shapes the company's trajectory and the future of AI safety.