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(Senior) Machine Learning Scientist (LLM & Agents)

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What you’ll work on

Full posting

We are looking for a Machine Learning Scientist (LLM & Agents) to join the Enterprise Solution Science Team.

You will contribute to the design, development, and productionization of one or more enterprise AI agents, including:

  • Design and build agent capabilities as reusable skills and MCP tools

  • Bring predictive models such as propensity, churn and recommendation into agent workflows

  • Build evaluation using test sets, LLM-as-a-judge and production trace review

  • Make agents faster and cheaper without losing answer quality

  • Write design docs, break work into reviewable PRs, and review teammates' code

From the employer’s posting
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.
You will contribute to the design, development, and productionization of one or more enterprise AI agents, including:
Build agent capabilities as reusable skills and MCP tools
Bring predictive models (e.g. propensity, churn, recommendation) into agent workflows
Build evaluation — test sets, LLM-as-a-judge, production trace review
Make agents faster and cheaper without losing answer quality
Write design docs, break work into reviewable PRs, and review teammates’ code

What you’ll bring

All qualifications

Core experience

  • Bachelor's degree or above in CS, ML, Statistics or a related field
  • 2+ years of experience in ML or software engineering
  • Hands-on experience building with MCP and agent skills
  • Hands-on experience with an agent harness such as Claude Agent SDK, OpenAI Codex or Google ADK
  • Strong SQL and data reasoning

Preferred experience

  • Experience with Databricks or Spark; LLM observability tools (e.g.
Qualification wording
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
Hands-on experience building with MCP and agent skills
Hands-on experience with at least one agent harness
Strong SQL and data reasoning
Experience with Databricks or Spark; LLM observability tools (e.g. Langfuse)
Education & alternatives
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
- 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

About Appier

Appier is a software-as-a-service (SaaS) company that uses artificial intelligence (AI) to power business decision-making.

In the employer’s words · Read in context

Apply at Appier

Posting record

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Open 6 monthsWe first saw this posting 204 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records
Live
First seen by us
Apr 15, 2026204 days ago
Recorded sightings
184
Last seen by us
Nov 5, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 204

Up longer than most. Similar jobs usually come down between day 6 and day 70.

Well past Appier’s usual: its jobs come down by about day 77.

From 4,444 similar postings that came down, and 10 of Appier’s. Coming down is not always a hire.

Appier is hiring

15 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Its jobs tend to stay up a long time
  • 7% of open jobs were reposted
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • Databricks
  • Spark
Where the posting names them
- 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
- Strategy Agent: Transform raw data into actionable, revenue-driving strategies and high-potential audience segments. - 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. - 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.
- 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
- 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

Your next step

Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.

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  • No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
  • Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.

Already applied? Track this application

Demand for ML Engineer roles

New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.

Weekly new ML Engineer postings at the same employers, from the week of August 17 to the week of October 26. The fewest in a week was 625, the most 1,094, and the latest week had 876.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 5 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

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.
    • 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.
    • 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.
  • Build agent capabilities as reusable skills and MCP tools, and design tool interfaces that agents use correctly and efficiently
  • Bring predictive models (e.g. propensity, churn, recommendation) into agent workflows and next-best-action suggestions
  • Build evaluation — test sets, LLM-as-a-judge, production trace review — and use it to decide what ships
  • Make agents faster and cheaper without losing answer quality, through context management, caching and model choice
  • Write design docs, break work into reviewable PRs, and review teammates’ code
  • Work closely with PMs, engineers and QA from planning to release

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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Taipei, Taiwan

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