Technical Account Managers are highly experienced Architects who bring both deep technical expertise and strong interpersonal skills to support a small portfolio of strategic clients (approximately 3 per TAM). They must be quick on their feet and able to put a positive spin on challenging customer situations, both in the boardroom with a client CTO and while sharing the command line with a client admin. They must be effective technically, both as communicators and doers. They must be capable of managing and maintaining a client relationship, while keeping a tight organizational watch over the technical aspects of their accounts.

How you'll make an impact

• Collaborate with the customer and the internal account team to jointly identify short and long-term priorities and develop the associated engagement plan
• Act as the primary technical advisor for client teams, providing guidance and hands-on support across a range of areas, including:
• Dataiku platform architecture and deployment
• Platform operations and upgrades
• Best practices for platform usage
• Security, data management, and compute resources
• ML-Ops, monitoring, and scaling strategies
• Assist clients in integrating the product into their systems and troubleshoot technical challenges
• Manage project milestones in partnership with Customer Success Managers and clients, contribute to deliverables, provide regular status updates and proactively identify and mitigate issues and risks
• Serve as a central point for client technical information and contribute to the account strategy alongside the broader account team
• Capture client feedback and feature requests to inform the Product and Engineering teams
• Advise client tech leaders on complementary technologies and long-term technical strategy.
• Explore and support advanced use cases involving Dataiku, such as edge computing, deep learning, and MLOps
• Support the growth and effectiveness of the Field Engineering team through documentation, process improvements, and knowledge sharing

What you'll need to be successful

• 7+ years of experience in a customer-facing technical role
• Native or bilingual proficiency in Japanese
• Fluent English communication 
• Strong communication and client relationship skills
• Experience supporting both pre- and post-sales engagements
• Proficiency in Linux system administration, including networking
• Experience with identity and access management tools (e.g., LDAP, Kerberos, Active Directory, IAM)
• Hands-on experience with cloud platforms (AWS, Azure, GCP)
• Hands-on experience with the Kubernetes ecosystem for setup, administration, troubleshooting and tuning
• Familiarity with the Hadoop and/or Spark ecosystems

How you'll stand out 

• Experience with Python
• Data-Science knowledge
• Basic knowledge of Java
• Familiarity with ML-Ops practices and tools

What does the hiring process look like? #LI-Hybrid #LI-AN1

• Initial call with a member of our Technical Recruiting team
• Video call with the TAM Hiring Manager
• Technical Assessment to show your skills (Home Test)
• Debrief of your Tech Assessment with TAM Team members
• Final Interview with the VP Field Engineering

#LI-AH1

#LI-JR1

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

Appier's Playable Ads team (https://www.appier.com/en/blog/what-is-playable-ads)  builds AI-powered playable and video ad experiences that drive dramatically higher engagement and conversion for the world's leading apps and games. We're looking for a Senior Backend Developer with deep expertise in distributed systems and modern backend architecture to design and build the scalable, high-performance services behind these experiences — from creative generation pipelines to the infrastructure that brings our ML models to production. You'll work across microservices, messaging systems, data persistence layers, and system performance optimization, while partnering with ML scientists on MLOps workflows to continuously ship and serve new model versions to end users. You'll also collaborate closely with frontend engineers and product managers to deliver seamless, cross-functional outcomes.

If you're passionate about building backend systems that operate at scale, thrive in cross-functional teams, and want your code to directly power AI-driven ad experiences reaching millions of users, we'd love to talk.

What You'll Work On

• Architect and develop robust, scalable backend services and APIs using ASP.NET Core, owning sizable projects from system design through production operation.
• Design efficient database schemas and optimize data access logic across SQL and NoSQL data stores.
• Implement message-driven communication with high reliability and fault tolerance.
• Partner with ML scientists to build and maintain MLOps pipelines — including model versioning, deployment, serving, and monitoring — ensuring new models are reliably delivered to production.
• Lead system designs, drive technical roadmap decisions, and provide guidance to team members.
• Proactively identify performance bottlenecks, profile critical components, and implement scalable solutions.
• Own and continuously improve system observability, incident management, CI/CD pipelines, and deployment workflows.
• Explore and integrate AI-assisted development tools and emerging technologies to boost team productivity.
• Participate in on-call rotation to ensure product reliability and scalability.

What We're Looking For 

[Minimum qualifications]

• Proficiency in C# and ASP.NET Core, or experience with other object-oriented languages (e.g., Java, Go) with willingness to cross-train in the .NET stack.
• Proven experience in distributed systems, microservice architectures, and designing scalable backend systems.
• Strong expertise in PostgreSQL, MongoDB, or equivalent SQL/NoSQL databases, with hands-on experience in ORM frameworks such as Entity Framework Core.
• Advanced knowledge of message broker systems such as RabbitMQ, Kafka, or NATS.
• In-depth understanding of IO-bound operations, threading, parallelism, and asynchronous programming.
• Practical experience with Docker, container-based deployment workflows, and cloud platforms (AWS, GCP, or Azure).
• Hands-on experience with CI/CD pipelines, observability tooling, and production operational practices.
• Demonstrated ability to write clean, well-documented code and solve complex problems analytically.
• Clear communicator who can explain technical decisions to both technical and non-technical stakeholders and enjoys working in a fast-paced, collaborative environment.
• Proficient in using LLM-powered development tools (e.g., GitHub Copilot, Cursor, ChatGPT) to accelerate productivity.

[Preferred qualifications]

• BS/MS degree in Computer Science or a related field.
• Experience with MLOps practices such as model serving infrastructure, A/B testing frameworks, or ML pipeline orchestration tools.
• Leadership experience such as mentoring junior engineers, leading code reviews, or facilitating agile processes.
• Experience with performance profiling and debugging tools, high-performance network services, or FFI integrations for performance-critical components.
• Solution-oriented mindset with a focus on system sustainability, and willingness to explore emerging technologies into backend systems.

[Language]

Fluent in English, Japanese language proficiency is not required.

Open to overseas candidates/Visa Support
This position can be based in Tokyo, Japan or Taipei, Taiwan. For international candidates, Appier's Japan office provides visa sponsorship to ensure a smooth relocation transition to Japan.

#LI-AK1

About the role 

We are seeking an experienced Machine Learning Lead to helm our Machine Learning team. 

In this pivotal role, you will be the engineering architect behind Vulcan’s core AI capabilities. You will act as the nexus between Research, Platform, and Product. Your mission is to translate cutting-edge findings on GenAI threats into robust, production-ready machine learning models that power our GenAI Security Guardrails (Blue Team) and Automated Vulnerability Assessment (Red Team). 

Crucially, you will serve as the bridge between deep tech and business strategy, articulating technical constraints (like FLOPS and latency) to leadership and clients while guiding the engineering direction. 

Key Responsibilities

1. Model Development & Optimization (Training & Fine-tuning):

• Research to Production: Collaborate with the Security Research Team to operationalize new threat detection techniques. They identify the "what" (e.g., new prompt injection patterns); you determine the "how" (model architecture, training strategy). 
• Fine-tuning & Adaptation: Lead the fine-tuning of Language Models (e.g., using LoRA/PEFT) to optimize for our supported muti-lingual languages and specific security intents.
• Multimodal Readiness: Prepare the system for Multimodal (Text + Image/Audio) capabilities. Evaluate and implement models to detect visual prompt injections and non-textual threats as the product evolves. 

2. MLOps& Data Infrastructure: 

• Enhance & Scale MLOps: Take ownership of our existing ML pipelines. Focus on optimizing and scaling CI/CD/CT workflows to improve training efficiency and deployment velocity. 
• Data Governance: Implement and enforce rigorous Data Versioning strategies (e.g., DVC) to ensure complete reproducibility of model artifacts and datasets.
• Monitoring & Reliability: Maintain rigorous monitoring for model drift and performance, ensuring high reliability in a production security environment.

3. Cross-Functional Implementation & Leadership:

• Platform Collaboration: Work closely with the Platform Engineering Team to integrate ML models into the broader product architecture. Ensure seamless interaction between model inference services and the main platform logic. 
• Team Leadership: Lead and mentor Machine Learning Engineers, fostering a culture of engineering rigor, code quality, and operational excellence.
• Resource Management: Manage GPU resources and compute budgets effectively for both training and inference workloads. 

4. Technical Strategy & Stakeholder Management:

• Translating Tech to Business: Act as the technical voice of the ML team. You must effectively explain complex ML concepts (e.g., FLOPS, quantization trade-offs, model latency vs. accuracy) to executive leadership and clients. 
• Cost-Benefit Analysis: Justify compute resource investments. Articulate the trade-off between infrastructure costs (GPU hours) and performance gains to non-technical stakeholders. 

Qualifications

• Experience: 5+ years in Machine Learning Engineering, with specific experience in leading technical projects or mentoring engineers.
• Communication & Business Acumen: Exceptional ability to distill complex technical topics (e.g., compute complexity, infrastructure costs) into clear, business-relevant insights for decision-makers.
• MLOps Proficiency: Proven experience in optimizing ML pipelines and infrastructure. Familiarity with tools like MLflow, Kubeflow, Airflow, and Data Versioning tools (DVC, etc.).
• Engineering First: Proficient in Python, Docker, and Kubernetes. You treat ML models as software artifacts that need testing and version control.
• NLP & LLM Expertise: Experience with Transformer architectures, Embeddings, and LLM fine-tuning. Familiarity with frameworks like PyTorch, Hugging Face, and vLLM.
• Language Support: Experience processing or fine-tuning models for multi-lingual environments. 

Nice to Have

• Multimodal Expertise: Experience working with Multimodal models (Image-to-Text, Text-to-Image, VLMs like CLIP, LLaVA).
• Security Awareness: Understanding of GenAI security threats (e.g., Prompt Injection).
• High-Performance Computing: Experience optimizing inference speed (quantization, distillation, vLLM) for real-time applications.
• Vector Database: Experience with Vector DBs for RAG applications. 

Why Join Us? 

• Innovative Environment: Be part of a company at the forefront of technology to provide security in GenAI, with opportunities to work on groundbreaking projects. 
• Growth Opportunities: Take your career to new heights with our career development programs and growth-focused culture. 
• Dynamic Team: Join a multi-cultural and dynamic team of dedicated professionals who inspire and support each other. 
• Compensation: Competitive salary and benefits package, commensurate with experience and performance. 

Application Process 

• If you're ready to embark on this exciting journey and contribute to shaping the future of GenAI security, please submit your resume outlining your relevant experience and motivation for applying.
• If you prefer a direct connection or have specific questions about our vision, feel free to reach out to our Co-founder, Alvin Kwock, via LinkedIn.