Applied AI Research (Agentic Systems)

Who We Are Looking For

NYB is seeking a senior engineer with genuine research instincts who treats our agent system as a living research problem. The agentic field reinvents its best practices every few months; we need someone who reads the frontier, has the taste to tell signals from hype, prototypes the promising ideas, and lands the ones that work in production — with evidence they made the system better.

You're not here to stand up infrastructure from scratch; we have a working system orchestrating 300+ tools and a small team running it. You're here to make it markedly smarter, more reliable, and more autonomous — and to raise the bar for the engineers building alongside you

Why This Role

Most agentic AI work optimizes against benchmarks that don't push back. Here, the agent's hypotheses get tested at the bench — its reasoning is answerable to real experimental results, not a leaderboard. We have a working system orchestrating 300+ tools; what we need is someone to keep it at the frontier. You'll read the latest in agentic research, decide what's real, and ship it into a system where "better" means a better experiment runs next week. If you want your work on autonomous agents to be grounded in reality instead of demos, this is the rare seat where it is.

Roles and Responsibilities

• Own the research-to-production loop for our agent: survey emerging agentic techniques, run structured experiments against them, and ship what proves out.
• Continuously upgrade core agent capabilities — planning, tool selection and reasoning, memory, multi-agent coordination — measured against real benchmarks, not demos.
• Build and own the evaluation harness that tells us whether a change is actually an improvement. This is the backbone of the role.
• Diagnose where the agent fails at the frontier of its current capability and design the fixes.
• Set technical direction for agentic best practices internally; review, mentor, and raise the quality bar for the existing engineers.
• Translate latest research into pragmatic engineering decisions under real reliability and cost constraints.

Yêu cầu ứng viên

Qualifications

• 6+ years software/ML engineering, with a recent track record building production agentic systems (planning, tool use, multi-step reasoning) — not prompt pipelines.
• Education: Master’s or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a highly quantitative field with a strong focus on autonomous systems or NLP.
• Demonstrated ability to take a technique from current research and land it in a production system, with measured impact.
• Strong command of agent frameworks and patterns (e.g., LangGraph, MCP, or equivalents) and the internals: tool definition, state/context management, orchestration.
• Rigorous evaluation — you've designed eval harnesses for non-deterministic systems and trust data over intuition.
• Fluent in reading and dismantling ML/agent papers; strong Python and system-design fundamentals.
• Preferred
• Exposure to scientific or research workflows, ideally drug discovery or other iterative experimental loops.
• Experience mentoring or setting technical direction for a small team.
• Background in RAG, memory systems, or multi-agent orchestration; bio/chem familiarity a plus.

Quyền lợi được hưởng

 Benefits

We provide a dynamic, fast-paced, and collaborative environment where problem-solving and agility are at the heart of what we do. Along with a competitive salary, we foster a culture that values ambition, confidence, and humility, consistently pushing the boundaries of innovation. If you're excited about working in a young, talented tech company and want to explore the world of AI and pharmaceuticals, we encourage you to apply.

• Competitive salary
• Workplace: 45-47 Tran Xuan Soan (Mon - Fri, 9h -17h)
• Build a professional network through collaborations with pharmaceutical companies, industry leaders, and academic experts.
• Work on impactful projects that address critical challenges in drug discovery and healthcare.