Machine Learning Scientist
Posted 23 May 2025
Advertised as “(Senior) Machine Learning Scientist (LLM & Agents)”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 2+ yrs
- Management level
- Individual contributor
No salary listed for this role.
Field of studyComputer Science, Machine learning, Statistics (or related)
Study levelBachelor's
Study levelMaster's or higher
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
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You will contribute to the design, development, and productionization of one or more enterprise AI agents, including:
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Marketing Agent: Empower marketers to improve conversion rates and drive revenue growth.
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Strategy Agent: Transform raw data into actionable, revenue-driving strategies and high-potential audience segments.
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Data Analysis Agent: Answer business questions by planning multi-step analyses and running SQL over large customer datasets, with every number traceable to the query that produced it.
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Sales and Service Agent: Drive sales conversion, increase average order value (AOV), and deepen brand engagement and customer loyalty through real-time, 24/7 AI-powered support.
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Build agent capabilities as reusable skills and MCP tools, and design tool interfaces that agents use correctly and efficiently
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Bring predictive models (e.g. propensity, churn, recommendation) into agent workflows and next-best-action suggestions
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Build evaluation — test sets, LLM-as-a-judge, production trace review — and use it to decide what ships
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Make agents faster and cheaper without losing answer quality, through context management, caching and model choice
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Write design docs, break work into reviewable PRs, and review teammates’ code
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Work closely with PMs, engineers and QA from planning to release