Member of Technical Staff

Collinear AI, Inc.; California (MO); On-site; USD 120,000 - 160,000; Full time

Job summary

Collinear AI, Inc. is seeking an MTS - Research to help build the data engine for frontier AI. You will develop high-fidelity environments and evaluation stacks that stress-test agents.

You will work across domains including Computer Use, Enterprise MCP/Toolcalling and Coding. Verifier Design, Simulated Personas, Benchmarking Personal AGI are among the research areas.

Join the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.

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.

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