Laban Kipkorir
I’m Laban Kipkorir, a Software Engineer focused on code quality, algorithm evaluation, and AI quality analysis.
About
I’m Laban Kipkorir, a Software Engineer focused on code quality, algorithm evaluation, and AI quality analysis. I review and refine programming problem statements for clarity and determinism, validate brute-force and optimized solution approaches, and build test suites to ensure edge cases are covered.
I also support end-to-end AI evaluation workflows, including response ranking, dataset review for supervised fine-tuning, and reinforcement learning with human feedback (RLHF). Beyond software, I have hands-on IT support and network administration experience, including troubleshooting, user access control, and supporting reliable, secure operations.
- 1 year's experience
- Joined November 2026
Industry experience
- Software & Internet
- Government
- Computers & Electronics
- Telecommunications
- Professional Services
- Education
Work experience· 4 roles
Reviewed and edited LLM-generated competitive-programming problem statements to make requirements precise and unambiguous. Checked LaTeX structure, wording, constraints, and whether expected outputs could be interpreted deterministically. Independently validated brute-force and optimized solutions for correctness, edge-case behavior, readability, efficiency, and style adherence. Developed and reviewed step-by-step reasoning from baseline to optimized approach, ensuring explanations matched the chosen data structures and algorithms. Wrote scripts to generate comprehensive test suites (30+ cases per task) and used results to detect weaknesses. Assessed potential plagiarism by comparing against public solutions (e.g., AtCoder/CodeChef). Verified solutions in Docker-based environments for consistent isolated testing and maintained task metadata for time/memory complexity and difficulty ratings.
Developed and maintained Java solutions to support AI model training, evaluation, and optimization workflows. Performed AI evaluations (Evals) to benchmark model performance and identify weaknesses. Evaluated and ranked AI-generated responses using quality, relevance, reasoning, technical accuracy, and instruction-following criteria, and produced detailed evaluation rationales. Supported supervised fine-tuning (SFT) by creating, reviewing, and maintaining task-specific datasets. Contributed to RLHF workflows to improve model quality and worked with researchers and annotators to keep evaluation standards and training data consistent.
Configured and supported routers, switches, firewalls, servers, and related network infrastructure. Monitored traffic, latency, uptime, and performance to identify and resolve connectivity problems. Supported network security through firewall controls and access permissions and participated in security audit activities. Managed user accounts and permissions for shared network resources. Supported backup strategies and disaster recovery practices to help protect critical systems and maintain stable connectivity.
Education
Certifications and awards
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