Software Engineer-Technical Advisor

A Software Engineer - Technical Advisor evaluates frontier AI coding models to uncover precisely where and why they break when working on real engineering tasks. Candidates spend their time reviewing model-generated pull requests, evaluating full agent session logs, and constructing hard, container-based test problems. Rather than writing code from scratch to ship product features, this role acts as a high-bar technical authority—using deep code review skills and rigorous written rationale to separate genuinely good software engineering from plausible, superficial AI code.

Typical Activities

• Audit model-generated pull requests (PRs) against real production repositories, documenting every identified issue alongside its severity and detailed technical rationale.
• Evaluating full coding-agent sessions to analyze what the model investigated, verified, assumed, or skipped.
• Designing and building container-based (Docker) technical benchmarks used to test AI models.
• Writing clear, original technical rationales explaining why code fails (all written work must be self-authored without AI text generators).
• Collaborating asynchronously with AI researchers to share findings and refine evaluation criteria.

Candidate Profiles & Disqualifiers

Target Profile

• 8+ years of production engineering experience preferred (exceptions only for clearly exceptional profiles).
• Backgrounds including Senior, Staff, or Principal Software Engineers, Tech Leads, or Open-Source Maintainers.
• Experience working in production codebases with a strict code review culture (startups, big tech, or open source are all acceptable).
• Polyglot adaptability: Heavy experience with Python and TypeScript is common, but must be comfortable dropping into unfamiliar languages weekly.
• Core Tooling: Demonstrated comfort using Docker, git, and the Command Line Interface (CLI) to reproduce, isolate, and debug results locally.
• Cross-Layer Fluency: Comfort working across backend, frontend, APIs, data, testing, or developer tooling.
• Strong written communication skills—able to articulate why code fails, not just how to fix it quickly.