AI Engineer

• Review and evaluate AI-generated code across programming languages and software engineering scenarios.
• Assess code for correctness, reliability, security, scalability, readability, and maintainability.
• Identify bugs, logical errors, incomplete implementations, edge-case failures, and architectural weaknesses.
• Analyze unfamiliar codebases and understand the impact of proposed changes.
• Review bug fixes, feature implementations, refactoring, API integrations, configuration changes, and database operations.
• Compare alternative implementations and determine whether they satisfy technical requirements.
• Rewrite or improve code to create high-quality reference solutions.
• Provide clear technical explanations of identified issues and recommended improvements.
• Develop evaluation criteria, technical annotations, and rubrics for code-quality assessment.
• Collaborate with researchers and engineering teams to design coding benchmarks and improve LLM evaluation methodologies.

Senior Software Engineer – AI Code Evaluation

• Own end-to-end program delivery across scope, timelines, quality, throughput, contributor performance, and cost.
• Design and manage workflows for coding datasets, agentic trajectories, RL environments, benchmarks, and rubric-based evaluations.
• Identify operational bottlenecks and improve workflows through better instructions, sequencing, incentives, review systems, and capacity planning.
• Define contributor requirements and partner with talent teams to source, assess, onboard, train, and ramp distributed software engineers.
• Build team-lead and reviewer structures for programs involving 100–1,000+ contributors.
• Own quality-control systems and analyze datasets to identify trends, systematic errors, and root causes.
• Act as a primary customer contact for AI labs, communicating progress, risks, quality trends, and recovery plans.
• Translate research objectives into practical task specifications and challenge requirements when they may not produce the intended evaluation signal.
• Use Python, SQL, or similar tools to automate quality sampling, defect analysis, throughput reporting, and operational reviews.
• Convert successful workflows into reusable playbooks, quality controls, evaluation assets, and contributor-management systems.
• Share operational learnings and mentor other program leads.

AI Engineer jobs in Vietnam

Senior Software Engineer – AI Code Evaluation

Technical Program Operations Lead – AI Engineering