Ai Engineer Jobs

About The Position

Requirements

• Bachelor's/Master's in Computer Science or related disciplines, or relevant experience in software architecture, design, development, and testing.
• Strong software engineering background with experience in large codebases, focusing on architecture, testing, and maintainability.
• Proficiency in building tooling across a modern stack, including linters, static analysis, CI pipelines, containerized build/test environments, and instrumentation/observability.
• Familiarity with agent instruction conventions such as AGENTS.md.
• Hands-on experience with AI coding agents (e.g., Claude Code, Codex, or similar) and an understanding of their strengths and weaknesses.
• Experience with spec-driven development, context engineering, agent orchestration, fitness functions, and developer-platform work.
• A systems mindset focused on fixing the environment rather than individual outputs, with the ability to translate quality standards into mechanical, repeatable rules.
• Judgement in choosing between deterministic controls (type checkers, linters) and inferential, LLM-based controls (AI code review, LLM-as-judge), understanding their trade-offs.
• Clear communication skills for articulating designs with architects and discussing strategy with teams.

Responsibilities

• Design, develop, deploy, and support components of the harness, including guides, feedback loops, guardrails, and shared context to ensure production-grade output from AI agents.
• Build feedforward guides such as agent instruction files, reusable skills, architectural rules, reference documents, and codemods to improve initial AI output accuracy.
• Develop feedback sensors like custom linters, static analysis tools, structural and architecture-fitness tests, verification loops, and LLM-as-judge reviewers to identify issues before human review.
• Manage the steering loop to prevent repeated mistakes by agents and maintain repository knowledge (docs, specs, context) for agent accessibility, combating drift.
• Contribute to quality gating, release criteria, and LLM testing to ensure AI-generated output meets quality and safety standards.
• Enhance observability into agent work and track key metrics such as cost per merged PR, time-to-merge for agent-assisted PRs, review velocity relative to PR size, defect escape rate, and agent-PR survival rate.
• Evaluate the stability, compatibility, scalability, interoperability, and performance of harness components.
• Continuously learn new techniques in agent-augmented engineering and serve as a technical expert and mentor to junior team members.

Benefits

• Highly competitive compensation, benefits and rewards programs
• Work/life balance
• Employee resource groups
• Social events