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• Take ownership of the full research-to-production cycle for our agent: explore new agentic methods, run structured experiments, and implement what delivers real results.
• Continuously improve the agent’s core capabilities — including planning, tool usage, reasoning, memory, and multi-agent coordination — based on real task performance and benchmark results, not demos.
• Build and maintain an evaluation framework to accurately measure whether changes lead to real improvements. This is a critical part of the role.
• Identify where the agent breaks down at its limits and design solutions to address those gaps.
• Contribute to leading the agent platform team by setting technical direction, mentoring peers, reviewing work, and raising overall engineering standards.
• Turn cutting-edge research into practical engineering decisions while balancing reliability, latency, and cost constraints.

Requirements

• PhD in Computer Science, Machine Learning, AI, or a similar quantitative field focused on NLP or autonomous platforms
• Senior experience building production-ready agent systems with real planning, tool usage, and multi-step reasoning (typically 6+ years in software/ML or equivalent hands-on research and deployment experience).
• Proven track record of taking research ideas and turning them into successful production features
• Deep understanding of agent architecture including state management, context handling, tool design, and orchestration (Frameworks like LangGraph or MCP are just tools, not the core skill.)
• Strong background in building evaluation tools for non-deterministic AI systems based on data rather than gut feel
• Solid Python skills, strong system design fundamentals, and the ability to digest advanced ML research papers
• Clear communication skills to help guide technical strategy, mentor teammates, and raise overall engineering quality

Nice to have

• Experience working in scientific or research-heavy domains such as drug discovery or iterative lab workflows
• Background in mentoring peers or helping set technical direction in smaller engineering teams
• Familiarity with RAG systems, memory architectures, multi-agent design, or basic knowledge of biology and chemistry

• Opportunity to build a strong professional network through collaboration with pharmaceutical companies, industry experts, and academic partners.
• Work on meaningful projects that tackle important challenges in drug discovery and healthcare.
• Enjoy 2 work-from-home days per month and daily company-provided lunch.
• Receive holiday and Tet bonuses, along with performance-based incentives.
• Full social insurance coverage based on total salary.