About Appier
Appier is an AI-native Agentic AI as a Service (AaaS) 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’re building agentic and recommendation products that power next-gen digital marketing. You will lead an engineering team at the intersection of ML systems, LLM/agent orchestration, and enterprise SaaS to deliver reliable, measurable outcomes for marketers—faster execution, smarter budget allocation, and differentiated brand performance.
Our goal is to build Marketing Agents (e.g., Audience Agent, Campaign Agent and Insight Agent) and Consumer-Facing Agents (e.g., Service Agent, Sales Agent), underpinned by a One Data Platform that enables trustworthy personalization and decisioning at scale.
What You'll build
Agentic Marketing Automation
- Natural language task assignment: users describe objectives and constraints in plain language; agents plan, execute, and report.
- Amplifying brand advantage: systems that help strong brands compound performance while giving others actionable levers to close gaps.
- One Data Platform integration: unify identity, events, product catalog, and campaign data to drive consistent decisions across products.
[Product Areas]
- Marketing Agents
- Audience Agent: segmentation, targeting strategies, cohort insights, next-best audience.
- Campaign Agent: multi-step campaign planning, creative/testing suggestions, launch & iteration, performance diagnosis.
- Consumer-Facing Agents
- Service Agent: post-purchase journeys, retention, support deflection, cross-sell triggers.
- Sales Agent: lead qualification, outreach recommendations, pipeline nudges (where applicable).
- Recommendation products: ranking, next-best-action, uplift modeling, personalized content/channel selection.
Responsibilities
Engineering leadership
- Lead a team of engineers (and partner closely with ML scientists) to deliver enterprise-grade agent and recommendation capabilities.
- Translate product goals into technical strategy, milestones, and execution plans; drive delivery with high quality and predictable cadence.
- Establish engineering excellence: code quality, testing, observability, incident response, and continuous improvement.
- Build an environment of humble, hungry, smart execution—measure outcomes, iterate quickly, reduce waste.
Agentic Systems & ML Product Delivery
- Design and ship agent frameworks: planning/execution loops, tool calling, memory, retrieval, safety guardrails, and evaluation.
- Define evaluation methodology for agents: offline benchmarks, online experiments, success metrics, and human-in-the-loop review flows.
- Assure solid integration with One Data Platform: data contracts, feature availability, privacy controls, and reliability SLAs.
- Partner with Product, Design, Data/ML, and GTM to ensure model behavior aligns with user needs and business goals.
Stakeholder & Culture
- Drive stakeholder alignment: connect engineering objectives to business outcomes (revenue, retention, customer value).
- Promote an autonomous culture: empower autonomous decision-making for both the team and the systems you build.
- Manage fast-paced experimentation while ensuring security, compliance, and enterprise readiness.
About you
[Minimum qualifications]
- 8+ years professional experience as a Software Engineer; 4+ years in a management/leadership role.
- Strong communication in English (written and verbal).
- Degree in Computer Science / Informatics or equivalent practical experience.
- Proven track record delivering enterprise-grade products (reliability, security, observability, performance).
- Experience working with or managing engineers and ML scientists.
- Experience delivering solutions using Agile principles.
[Preferred qualifications]
- Experience building LLM/agentic systems in production (tool use, RAG, orchestration, evaluation).
- Hands-on background with ML systems (recommendation, ranking, attribution, uplift, LTV, forecasting) and their productionization.
- Familiarity with data platforms (event pipelines, identity resolution, feature stores, lakehouse/warehouse) and data governance.
- Experience with A/B testing and causal measurement; comfortable making tradeoffs using metrics.
- Exposure to MA/CRM domains: segmentation, journeys, omnichannel messaging, campaign ops, budget pacing.
- Experience in leading hybrid (onsite/remote) engineering team.
About Us (Appier Engineering)
At Appier Engineering, we aim to be humble, hungry and smart. We improve through constant measurement and feedback—building less and talking more to ensure outcomes users love while reducing waste. We innovate by failing well, adapt quickly, and stay efficient and responsive when needed. We’re looking for an Engineering Manager who embodies these values through work and life experiences.
