Agent Reliability & Evaluation Algorithm Engineer

Shopee; Singapore; Vor Ort; SGD 90.000 - 130.000; Vollzeit

Zusammenfassung

Shopee is seeking an expert to design and own the evaluation system for LLM Agents, including benchmark construction and LLM-as-Judge with human annotation. You will build reliability analyses across multi-step trajectories and lead evaluation-driven optimization of agents and tools.

The role involves leading post-training efforts for LLMs, building RAG and tool-layers, and translating research into deployed solutions with a focus on frontier AI and end-to-end quality.

Qualifikationen

• Master’s degree or above in Artificial Intelligence, Computer Science, Mathematics, or related field.
• At least 1 year of full-time industry experience building and deploying production LLM Agent systems with evaluation design.
• Experience fine-tuning LLMs via SFT and DPO, and building RAG/retrieval systems.
• Proficient in Python; familiarity with PyTorch.
• Strong analytical skills and experimental design; collaborative mindset.

Aufgaben

• Design and own the evaluation system for LLM Agents and benchmarks.
• Build Agent reliability analysis with error attribution across multi-step trajectories.
• Drive evaluation-guided Agent optimization including Harness design, tool calling, and memory.
• Lead evaluation-driven post-training of LLMs for vertical domains (SFT, DPO, RL).
• Build and optimize RAG/retrieval systems and the Agent tool layer; assess impact.
• Explore frontier research and drive a full loop from research to deployment.

Kenntnisse

LLM Agent design

Evaluation metrics

Python programming

Statistics

Experiment design

Team collaboration

Ausbildung

Master’s degree in AI/CS/Math

Tools

verl

vLLM

FSDP

LLaMA-Factory

Jobbeschreibung

Job Description

• Design and own the evaluation system for LLM Agents, including benchmark construction, LLM-as-Judge with human annotation, and regression/comparison pipelines. Continuously monitor product metrics to guide Agent and model optimization.
• Build Agent reliability analysis, including error attribution and uncertainty estimation across multi-step trajectories, and turn failure cases into targeted improvements.
• Drive evaluation-guided Agent optimization, including Harness design (planning, verification, self-refine), tool calling, context engineering, and memory.
• Lead evaluation-driven post-training of LLMs for vertical domains (SFT, DPO, RL), including data pipelines and reward model design.
• Build and optimize RAG/retrieval systems and the Agent tool layer (search, code interpreters, sandboxes, MCP) and evaluate their impact on end-to-end Agent quality.
• Explore frontier research in Agent reliability and evaluation and drive a full closed loop from research to deployment.

Requirements

• Master’s degree or above in Artificial Intelligence, Computer Science, Mathematics, or a related field.
• At least 1 year of full-time industry experience building and deploying production LLM Agent systems (task planning, orchestration, tool calling), including designing evaluation sets and metrics (LLM-as-Judge, human annotation, error attribution) to drive iteration.
• Hands-on experience fine-tuning LLMs via SFT and DPO (GRPO/RL a plus), and building and optimizing RAG/retrieval systems (recall, ranking, embedding fine-tuning).
• Good programming skills; proficient in Python (SQL and PyTorch are a plus). Familiar with training and inference frameworks such as verl, vLLM, FSDP, or LLaMA-Factory.
• Strong analytical skills, with a solid grasp of statistics and experiment design. Sustained curiosity about frontier AI, strong self-drive, and the ability to collaborate closely with teams to drive a full closed loop from research to deployment.