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(Senior) Machine Learning Scientist (LLM & Agents)
Appier, Taipei, Taiwan
About the job
About Appier
Appier is a software-as-a-service (SaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.
About the Role
We are looking for a Machine Learning Scientist (LLM & Agents) to join the Enterprise Solution Science Team. This team focuses on applying cutting-edge ML technologies to real-world marketing problems by combining them with omnichannel customer data. In this role, you will own agent features end to end — from problem definition and design, to shipping, to measuring and improving quality in production.
What You’ll Work On
- You will contribute to the design, development, and productionization of one or more enterprise AI agents, including:
- Marketing Agent: Empower marketers to improve conversion rates and drive revenue growth.
- Strategy Agent: Transform raw data into actionable, revenue-driving strategies and high-potential audience segments.
What We’re Looking For (Minimum Qualifications)
- Bachelor’s degree or above in Computer Science, Machine Learning, Statistics, or a related field
- 2+ years of experience in ML or software engineering roles
- Has built at least one LLM or agent application end to end — at work, in research, or as a side project — and can explain how its quality was evaluated and improved
- Hands-on experience building with MCP and agent skills (tools, instructions and reference files an agent loads on demand)
- Hands-on experience with at least one agent harness (the runtime that drives the agent loop of model calls, tools and context), e.g. Claude Agent SDK, OpenAI Codex, or Google ADK — and an understanding of how the harness, tool design and context shape agent behavior
- Can design evaluation for non-deterministic LLM output, including test sets, metrics, and the limits of LLM-as-a-judge
- Strong SQL and data reasoning: can write and review analytical queries and tell when a result is wrong
- Can profile and reduce end-to-end latency and cost of an agent — e.g. fewer model round-trips, better tool design, caching, model choice — without hurting answer quality
- Writes production-quality Python with tests
- AI-native development: works daily with coding agents that read the codebase, run tests and iterate on their own, while you set the scope and review the diffs. Can explain and correct what the agent produced
- Clear written communication in design docs and PR descriptions
Preferred Qualifications
- Has shipped an LLM or agent application to real users in production
- Has run coding agents with more autonomy — letting them carry whole features end to end under constraints you set, or building pipelines where agents build, test and ship with humans designing the system
- Agent memory: persisting and retrieving user or task context across sessions
- Feedback loops: turning user feedback and production traces into improved skills, tools or prompts, with human review
- Context engineering: progressive disclosure, context management for long multi-turn sessions, prompt caching
- Lightweight decision models (e.g. Jev) for routing, intent classification or caching
- Guardrails and verification for agents that act on enterprise data
- Background in predictive modeling, segmentation, or experiment design; understands correlation vs causation
- Experience with Databricks or Spark; LLM observability tools (e.g. Langfuse)
- Master’s or PhD degree
- Industry experience in MarTech and passion for building customer-centric products
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Source: the Appier Greenhouse job board. We last checked this job 9 h ago. We first saw it 2 days ago. A job on a job board does not always mean that the team interviews people now. We found this job board in a public list. We did not yet compare it with the careers page of the company.
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