Applied AI Engineering Manager

Company Description YKK AP Technologies Lab (NA) Inc. researches and develops advanced digital technologies for the construction products and building materials industry. The organization focuses on digitizing the entire product lifecycle, from initial design and construction through final installation. By integrating IoT devices, AI-driven analysis, and remote-control networks, the team has pioneered a “Virtual Factory” initiative that uses Digital Twin environments for virtual simulations and process optimization. These innovations enable the creation of complex, efficient supply chains and significantly improve production performance. The company is also dedicated to cultivating global, digital-ready talent and exploring next-generation materials to shape the future of the industry.

Summary:

We are seeking on Applied AI Engineering Manager to lead the development of AI-driven solutions for manufacturing. This role sits at the intersection of generative AI, industrial systems, and real-world operations. You will define technical direction, lead a small team of engineers, and translate emerging AI capabilities into practical applications on the factory floor. This is a “0→1” environment where ambiguity is high, and speed of learning matters as much as depth of expertise. We are looking for a leader who has deep experienced in one core domain (e.g., generative AI or machine learning systems), with working knowledge across adjacent areas such as robotics, simulation, or computer vision.

Essential Duties and Responsibilities include the following. Other duties may be assigned.

• Lead a multidisciplinary team of AI, physical AI, machine vision, and software engineers as a player-coach.
• Define and execute the applied AI roadmap aligned with manufacturing priorities.
• Translate between the research team and YKK AP’s manufacturing operations — identifying real problems on the factory floor, framing them as research questions, and shepherding promising prototypes into deployed internal applications.
• Lead the design and implementation of generative and agentic AI systems built on foundation model APIs (e.g., Anthropic Claude, OpenAI), including retrieval-augmented generation (RAG), fine-tuning, and multi-agent orchestration with frameworks such as LangGraph.    
• Partner with the hardware engineering team on machine vision work that intersects with the lab’s physical AI and digital twin programs — providing technical direction on perception components within physical AI systems while leveraging the hardware team’s deep machine vision expertise on optics, sensors, and industrial inspection.
• Manage project scope, timelines, and resources for the team’s work, balancing exploratory research with delivery of internal applications.
• Define and enforce engineering best practices appropriate for an applied research environment, including version control, experiment tracking, reproducibility, code review, and responsible AI considerations such as bias, safety, and transparency.
• Present research progress, technical strategy, and prototype demonstrations to engineering leadership, manufacturing stakeholders, and senior leaders within YKK AP.

Supervisory Responsibilities:

This job has direct supervisory responsibilities.

Direct Technical Mentorship:

Provide high-level guidance to engineers across the team’s focus areas, raising the bar through code reviews, architectural design reviews, research paper readings, and hands-on pairing on complex technical bottlenecks.

Talent Development & Performance: Manage the full employee lifecycle for the AI team, including recruiting and hiring, setting performance goals, conducting regular 1:1s, and providing career development coaching.

Resource Allocation: Oversee the distribution of tasks and individual workloads, ensuring team members are assigned to projects that align with both manufacturing priorities and their own professional growth.

Culture Leadership: Foster a high-performance culture of curiosity, rigorous experimentation, and psychological safety, encouraging the team to take calculated risks in applied research.

Education/Experience:

• Master’s degree minimum in Computer Science, Computer Engineering, Robotics, Artificial Intelligence, Data Science, or a related field. Ph.D. is welcomed but not required.
• 7-10 years of software design and engineering experience in industry, including 3+ years leading or technically directing engineering teams.
• Expertise in navigating the rapidly shifting AI landscape to identify and implement production-grade techniques within end-to-end physical systems.
• Demonstrated ability to envision how emerging AI capabilities apply to physical-world or manufacturing problems, with examples of moving research prototypes into deployed applications.
• Strong hands-on experience in one of the following: Generative AI / LLM applications (RAG, agents, fine-tuning), or Machine learning systems in production environments.
• Working knowledge of machine vision sufficient to direct the perception components of physical AI systems and collaborate effectively with the hardware engineering team’s machine vision specialists — including familiarity with deep learning-based perception (PyTorch, OpenCV, YOLO and successors) and a basic grasp of optics, sensors, and lighting.
• Strong software engineering fundamentals — proficiency in Python and at least one systems language (C++ preferred), source control, code review, containerization (Docker), and cloud compute (AWS, Azure, or GCP).
• Excellent technical communication — able to explain complex AI systems credibly to senior engineers and clearly to non-technical manufacturing stakeholders.
• Strong analytical and problem-solving skills, with the ability to adapt to changing project needs, technologies, and research priorities.

Bonus Points:

• Prior experience applying AI to manufacturing, industrial automation, or other physical-world domains (logistics, autonomy, energy, defense, healthcare imaging).
• Experience working in applied research or innovation environment. 
• Hands on experiences with MLOps and ML lifecycle tooling (MLflow, Weights & Biases, DataBricks, etc.). 
• Exposure to simulation-to-real pipeline or digital twin technologies.

Physical Demands:

The physical demands described here are representative of those that must be met by an individual to successfully perform the essential functions of this job. An individual must occasionally lift and/or move up to 25 pounds. Proper lifting techniques required. Specific vision abilities required by this job include close vision, distance vision, peripheral vision, depth perception and ability to adjust focus. While performing the duties of this job, an individual is regularly required to sit. An individual is frequently required to use hands to finger, handle, or feel and talk or hear. An individual is occasionally required to stand; walk; reach with hands and arms; climb or balance and stoop, kneel, crouch, or crawl. The travel required for this position is 15%.

Work Environment:

The work environment characteristics described here are representative of those individual encounters while performing the essential functions of this job. An individual may be required to wear appropriate safety protective equipment at the proper times and in the proper environments as outlined by safety program rules. The noise level in the work environment is usually moderate. While performing the duties of this job, an individual is occasionally exposed to work in high, precarious places and outdoor weather conditions.

*Applicants must be “legally authorized to work in the United States without sponsorship.