Senior AI Engineering Lead (PhD)

Software Development Developer ML AI/Artificial Intelligence AI/ML Machine Learning

Icon salary Salary
Negotiable
Icon Location Location
Ha Noi, Vietnam

Benefits

13th month salary 13th month salary
Full social insurance Full social insurance
Other benefits Other benefits
Yearly salary review Yearly salary review
Travel/company trips Travel/company trips
Laptop/desktop for works Laptop/desktop for works
Performance bonus Performance bonus
Extra health insurance Extra health insurance

Job Overview And Responsibility

NYB is looking for a senior AI engineer leader with the research depth of a scientist and the instincts to lead. You treat our agent as a living research problem, not a shipped feature. The agentic field rewrites its own best practices every few months; we need someone who reads the frontier, has the taste to separate signal from hype, prototypes the promising ideas fast, and lands the ones that hold up in production — with evidence they made the system better. You won't stand up infrastructure from scratch. We already run a working agent orchestrating 300+ scientific tools, with a small, strong team behind it. You're here to make it markedly smarter, more reliable, and more autonomous — and to help lead the engineers building it, raising the bar for how the whole team works. - 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 tasks and bench outcomes, not demos. - Build and own the evaluation harness that tells us whether a change is genuinely an improvement. For a system answerable to experiments, this is the backbone of the role. - Diagnose where the agent fails at the edge of its current capability, and design the fixes. - Help lead the agent platform team: set the technical direction for agentic practice internally, review and mentor, and raise the quality bar for the engineers you work alongside. - Translate frontier research into pragmatic engineering calls under real reliability, latency, and cost constraints.

Required Skills and Experience

- PhD in Computer Science, Machine Learning, Artificial Intelligence, or a highly quantitative field with a strong focus on autonomous systems or NLP. - A senior track record building production agentic systems — real planning, tool use, and multi-step reasoning, not prompt pipelines. Depth counts more than title or exact tenure (roughly 6+ years of software/ML work, or equivalent research-and-shipping depth). - Demonstrated ability to take a technique from current research and land it in a production system, with measured impact. - Deep command of how agents actually work — tool definition, state and context management, orchestration, multi-step control — and the judgment to choose or build the right pattern. (LangGraph, MCP, and whatever replaces them are tools, not the skill.) - Rigorous about evaluation: you ve designed eval harnesses for non-deterministic systems and you trust data over intuition. - Fluent reading and dismantling ML/agent papers; strong Python and system-design fundamentals. - The judgment and communication to help lead a technical team — set direction, review others' work, raise the bar

Why Candidate should apply this position

- Competitive salary (negotiable based on experience) - 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. - Employees are entitled to 2 work-from-home days per month, along with daily lunch provided by the company. - Holiday & Tet bonuses; performance-based bonus - Social insurance contribution on full salary

Preferred skills and experiences

- Exposure to scientific or research workflows, ideally drug discovery or another iterative experimental loop. - Prior experience mentoring or setting technical direction for a small team. - Background in RAG, memory systems, or multi-agent orchestration; bio/chem familiarity a plus.

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CEO

Interview process

Screening -> 1-2 rounds of technical interview

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