Member of Technical Staff - Software Engineer, AI Capabilities

preference model • Canada

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AI Summary

Preference Model is seeking a Software Engineer to build complex RL training environments for AI models. This role involves designing, coding, and refining environments that expose AI failures in software engineering tasks. Requires deep software engineering expertise, Python proficiency, and experience with coding agents.

Key Highlights

Builds software engineering environments to train AI models.

Owns complex tasks end-to-end with full autonomy.

Blends research and engineering with a focus on novel approaches.

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Key Responsibilities

Design, build, and refine RL tasks across their full lifecycle.

Own the hardest environments on the roadmap: multi-step workflows, realistic stakeholder interactions, large codebases with real conventions and technical debt, and system design problems.

Direct coding agents heavily in your day-to-day work, evaluate their output critically, and recognize when they are failing in subtle ways.

Distinguish genuine model capability gaps from grader or environment issues, and redesign tasks to target deeper, more subtle failure modes.

Contribute to the shared infrastructure and tooling that the environments team depends on.

Mentor newer engineers on the team as it grows.

Technical Skills Required

Python

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Benefits & Perks

Competitive cash and equity compensation

Health, vision, dental, benefits

401K match

Lunch provided everyday onsite

Weekly snack orders

Visa sponsorship & relocation support available

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Nice to Have

Deep specialty expertise in an area that current models struggle with (distributed systems, low-level performance, security, compilers)

Been an early engineer at a previous startup, shipped independently, and want to do it again in AI.

Spent significant time building with coding agents, written about their failure modes, or contributed to agent evaluation work.

Job Description

Preference Model is building automated ML research engineering.Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions.Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.

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