MTS - Research

Collinear AI, Inc.; Sunnyvale (CA); On-site; USD 160,000 - 400,000; Full time

Benefits offered by this job

Frontier projects

Equity

Direct impact

Competitive salary

Job summary

Collinear is seeking a highly capable MTS - Research to build data engines for frontier AI. You will craft ultra-realistic SimLabs and evaluation stacks used by leading AI labs to stress-test advanced agents.

The role spans computer use, enterprise tools, and coding with emphasis on reliability and safety. You will collaborate with founders and researchers, tackle RLHF/RLAIF domains, and contribute to scalable data pipelines and long-horizon agentic environments.

Qualifications

• Proficient in Python development and CLI-first workflows.
• Strong foundation in ML methods, model evaluation, and reproducibility.
• Experience conducting empirical research and producing data-driven results.
• Proven ability to work across domains with researchers and engineers.

Responsibilities

• Design and implement ultra-realistic, long-horizon simulation environments (SimLabs).
• Develop rigorous policy-aware verifiers and evaluations beyond benchmarks.
• Plan and execute post-training runs (CPT, SFT, RL) to demonstrate frontier performance.
• Collaborate daily with founders and researchers to shape the roadmap and advance AI reliability.
• Analyze model failure modes and create data pipelines that scale with test-time compute.

Skills

Python

ML foundations

Research experience

Reinforcement Learning

Education

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

Job description

About Collinear

At Collinear, we help teams fearlessly ship AI.

About the Role

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.

• Work on analyzing model failure modes and creating frontier data pipelines which scale with test-time compute.

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 seed-backed startup, your work directly shapes the company's trajectory and the future of AI safety.

Collinear is an equal opportunity employer and values diversity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.

The base salary range for this role in California is $160,000 to $400,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.

Collinear provides reasonable accommodations for candidates with disabilities throughout the application and hiring process. If you need an accommodation, please contact us.

Pursuant to applicable local ordinances, we will consider qualified applicants with arrest and conviction records.