Our CTO Office AI Research Group is looking for a motivated advanced BSc student to join our team as a Data Preprocessing Engineer. You will play a foundational role in building the data infrastructure that powers our AI research - working directly with large-scale, real-world datasets at the intersection of machine learning and semiconductor chip design.
job requisition id
2604499
This is a hands-on, high-impact student position for someone who enjoys getting into the details of messy data and turning it into clean, structured, and model-ready assets.
What You Can Expect:
Collect, clean, normalize, and sanitize large-scale chip design datasets from diverse sources and formats
Build and maintain scripts and pipelines to automate repetitive data extraction and transformation tasks
Parse and process a wide variety of file formats (JSON, CSV, Parquet, logs, and domain-specific EDA formats)
Navigate complex codebases and directory structures to locate, understand, and extract relevant data
Work with structured and semi-structured data using SQL and Python-based tooling
Collaborate with researchers and engineers to understand data requirements and deliver model-ready datasets
Document data sources, transformation logic, and dataset versions in shared repositories.

We are looking for a highly motivated AI Research Student for our Robotics & Simulation AI team. You will be passionate about data science, AI quality measurement, and the intersection of AI with high-accuracy industrial domains. This role is ideal for someone who grows with challenging existing assumptions, defining rigorous evaluation methodologies, and pushing AI systems toward measurable reliability. You will combine strong data science and research skills with hands-on experimentation in a production AI product that controls robotic manufacturing simulations.

Key Responsibilities

AI Quality & Stability Research

Define and implement metrics to measure the stability, accuracy, and reliability of our production AI Copilot (PSCoPilot)
Design evaluation frameworks for AI agents operating in safety-critical robotics and simulation environments
Build observability and tracing pipelines using platforms like Langfuse to monitor Copilot performance, token usage, latency, and tool-calling success rates
Analyze failure modes, hallucination patterns, and tool-calling accuracy across 48+ MCP-exposed robotic operations
Develop automated regression and benchmark pipelines to track Copilot quality over time

Data Science & Experimentation

Conduct statistical analysis on Copilot interactions (tool selection accuracy, collision detection correctness, path planning success rates)
Research and apply techniques from AI evaluation literature (LLM-as-judge, human-in-the-loop validation, domain-specific benchmarks)
Leverage LangChain and similar orchestration frameworks to prototype evaluation chains, structured agent workflows, and comparison experiments
Explore how multi-layer agent architectures (fast triage + deep execution) affect accuracy and cost trade-offs
Challenge the team with data-driven insights on where the Copilot underperforms and why

Domain AI Research

Investigate how to improve AI reasoning for robotics tasks requiring high precision (welding, clearance checking, reachability analysis)
Research grounding techniques that reduce errors in domains with zero tolerance for incorrect outputs
Explore and apply new AI technologies (GenAI tools, AI coding assistants, agents, MCP-based solutions) to enhance Copilot capabilities
Validate technical feasibility through hands-on experimentation with our production MCP bridge and two-layer streaming agent engine

Communication & Documentation

Document findings, benchmarks, and technical recommendations
Present results to technical and business partners
Collaborate with the team to translate research insights into production improvements