Senior Frontend Engineer (Next.js / TypeScript), AI/ML-powered IoT Platform

Who We Are

JIG-SAW operates 24/7 Operations Centers in Japan and Canada that proactively monitor systems and deliver live incident response—keeping web services and IoT environments stable and high-performing.

• Corporate site: https://jig-saw.com/en
• NEQTO.ai: https://neqto.ai

About the Role

We are looking for a Senior Frontend Engineer who doesn’t just build "pages," but crafts high-performance interfaces for our multi-tenant SaaS IoT platform. You will own the frontend architecture end-to-end—building dashboards that handle high-velocity data, complex onboarding workflows, and live monitoring views.

This role requires a blend of technical optimization (making things fast) and product thinking (making things intuitive). You’ll collaborate with Backend and AI teams to bridge the gap between complex data streams and a seamless, responsive user experience.

What You’ll Do

• Performance Optimization: Proactively audit and optimize UI performance (Core Web Vitals, bundle sizes, and execution time) to ensure the platform remains snappy while handling massive IoT datasets.
• Responsive Architecture: Design and implement a truly responsive UI that functions flawlessly across desktop, tablet, and mobile—ensuring field engineers and executives alike have the data they need.
• User Flow & UX: Own the user flow of critical features; you'll think through how a user moves from "device registration" to "alert monitoring" to minimize friction and maximize clarity.
• Modern Stack Ownership: Evolve our architecture using Next.js 15 (App Router), React 18+, and TypeScript 5.
• Design Systems: Scale a modern design system using (Radix UI + Tailwind CSS v4).
• Real-time Data: Implement streaming updates and efficient caching strategies to display live sensor data without draining the user's browser resources.

Required Qualifications

• Experience: 5+ years of professional experience with Next.js/React and TypeScript.
• Modern Tooling: Deep expertise in Next.js 15 (App Router), React 18+, and Tailwind CSS v4.
• Responsive Mastery: Expert-level knowledge of CSS/Tailwind for building complex, responsive layouts that don't break on edge-case screen sizes.
• Optimization Mindset: Proven track record of optimizing heavy client-side applications (virtualization, memoization, and efficient data fetching).
• Education: Bachelor’s degree or higher in Computer Science (or closely related field).

Nice-to-Have

• User Flow Design: Experience mapping out and implementing intuitive user journeys for complex B2B or SaaS products.
• Data Visualization: Experience with D3.js, Recharts, or Canvas for real-time sensor data visualization.
• IoT Awareness: Experience with monitoring systems, device management, or telemetry dashboards.

Portfolio Requirement (Mandatory)

Applications without a portfolio will not be considered. Please provide links to:

• GitHub or GitLab profile.
• Live product sites or technical demos.
• Case studies highlighting your role in optimization or UI architecture.

Candidates must also pass a third-party programming assessment as part of the screening process.

Job Type: Contract

Pay: $100,000.00 - $150,000.00 per year

Benefits:

• Dental insurance
• Health insurance
• Paid time off
• Vision insurance

Application Question(s):

• [Required] This is the first-round screening question #1. If this field is left blank, you will not be considered.

The key evaluation criterion is whether you can explain your reasoning logically. We value how you think more than what you already know. If a response is detected to have been generated using ChatGPT or an equivalent tool, it will be automatically rejected.

For the function below, propose technical approaches you would use to implement it. Assume real production use: consider performance, scalability, operating cost, and LLM context-window limits.

On an IoT data dashboard, display:

1) Insights through the previous day (e.g., findings, early signs of failure, predictive maintenance).

2) A list of alerts through the previous day (not simple threshold checks, but early failure signs and predictive maintenance).

• [Required] This is the first-round screening question #2. If this field is left blank, you will not be considered.

The key evaluation criterion is the same as #1.

For the function below, propose technical approaches you would use to implement it. Assume real production use: consider performance, scalability, operating cost, and LLM context-window limits.

Enable users to obtain on-demand insights (findings, early signs of failure, predictive maintenance) by asking a chatbot, based on all data collected to date.

• [Required] This is the first-round screening question #3. If this field is left blank, you will not be considered.

The key evaluation criterion is the same as #1.

For the function below, propose technical approaches you would use to implement it. Assume real production use: consider performance, scalability, operating cost, and LLM context-window limits.

From the data format sent by unknown IoT devices, auto-register/provision devices.

1) Automatically detect and register the device with its metadata when multiple sensor values compressed/obfuscated by encoding them as binary/hex.

2) Automatically detect and register the device with its metadata when multiple sensor values are human-readable (non-binary/non-hex) payloads.

• [Required] This is the first-round screening question #4. If this field is left blank, you will not be considered.

The key evaluation criterion is the same as #1.

From the data format sent by unknown IoT devices, auto-detect/map sensor attributes.

1) Automatically detect sensor attributes (e.g., temperature), normalize them, and associate attributes with the database scheme. Assume multiple sensor values compressed/obfuscated by encoding them as binary/hex.

2) Automatically detect sensor attributes (e.g., temperature), normalize them, and associate attributes with the database scheme. Assume multiple sensor values are human-readable (non-binary/non-hex) payloads.

• [Required] This is the first-round screening question #5. If this field is left blank, you will not be considered.

The key evaluation criterion is the same as #1.

For the function below, propose technical approaches you would use to implement it. Assume real production use: consider performance, scalability, operating cost, and LLM context-window limits.

Settings recommendations: a feature that recommends configuration settings the user tends to prefer.

• [Required] Please describe in detail your professional experience using generative AI in Industrial IoT environments such as smart factories, smart buildings, logistics, and energy management. If this field is left blank, you will not be considered.
• [Required] Please describe in detail your professional experience using machine learning in Industrial IoT environments such as smart factories, smart buildings, logistics, and energy management. If this field is left blank, you will not be considered.

Education:

• Bachelor's (Required)

Experience:

• professional ML (shipped, deployed, monitored): 5 years (Required)
• professional GenAI (LLMs/RAG): 3 years (Required)
• industrial IoT (e.g., smart factories, logistics): 5 years (Required)

Work Location: Remote