Senior Applied AI Research Engineer

ANYWORK Co., Ltd

Job overview and responsibility

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. - 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.

Required skills and experiences

- 6+ years software/ML engineering, with a recent track record building production agentic systems (planning, tool use, multi-step reasoning) — not prompt pipelines. - 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 skills and experiences

- 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.

Why Candidate should apply this position

- Competitive salary - Workplace: This role is based in our Hanoi office, located in the vibrant Old Quarter (Mon - Fri) - 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.

Report to

CEO

Interview process

Screening -> 1-2 rounds of interview