Machine Learning Engineer (Agentic AI)

About the Company

The AI Transformation Team creates new ways of solving engineering problems and workflows based on AI. We develop systems that can autonomously explore and improve engineering problems using various AI technologies, focusing on expanding AI-based problem-solving capabilities across software, hardware, and systems.

Responsibilities

• Research and implement agent behaviors including agent planning, tool use, memory, reasoning, and self-evolving mechanisms
• Develop multi-agent workflows and orchestration to allow multiple agents to collaborate and explore solution spaces
• Develop evaluation systems that automatically execute, verify, and evaluate agent-generated results to enable iterative system improvement
• Perform agent evaluation and optimization using LLM-as-a-judge, task-specific metrics, and benchmarks
• Experiment with and apply various post-training techniques such as SFT and RL
• Implement rapidly changing Agentic AI and post-training research into forms applicable to real-world engineering problem-solving

Requirements

• Bachelor's degree in Computer Science or a related field
• Experience with model training or experimentation using at least one ML framework such as PyTorch, JAX, or TensorFlow
• Experience implementing or experimenting with LLM or Agentic AI-based systems
• Ability to quickly understand latest research papers and translate them into practical implementations

Preferred Qualifications

• Master's or PhD in Computer Science or a related field
• Experience in agent system development: tool use, reasoning, memory, planning, or multi-agent orchestration
• Experience in LLM post-training: SFT, RL, or preference optimization
• Experience building agent evaluation systems: benchmark design, LLM-as-a-judge, or automated evaluation
• Experience using or developing agent frameworks like LangGraph, AutoGen, or Google ADK
• Experience with distributed training, inference optimization, or large-scale experimentation
• Experience applying Agentic AI and post-training research to real-world problem solving