【Computer Vision Engineer】JLPT N2 | Flextime & Hybrid Work

★【Computer Vision】 Research Engineer | AI Development Platform Provider

• Business Level Japanese Required (N2 Level)

◆ Flextime & Hybrid Work

◆ Start-up Company

◆ Annual salary: 6 million yen - 15 million yen

-------------【About the company】-------------

Adopting a "Data-Centric AI Development" approach, the company specializes in the collection and creation of "high-quality data," which directly dictates the accuracy and safety of AI models. While it is often said that data preparation accounts for 80% of the AI development process, the company addresses this challenge through its proprietary platform.

1. In-house Product: AI Data SaaS

The company operates a platform where crowdworkers from around the world participate in annotation (creation of training data) by utilizing a mechanism similar to "poi-katsu" (reward-earning) apps. By leveraging proprietary quality-control algorithms, the company achieves low-cost and rapid data collection.

2. Data Collection and Annotation Services

The company handles a wide range of projects, from "0 to 1" new development to large-scale operational tasks based on well-defined requirements. It addresses diverse orders in cutting-edge technical fields—such as Large Language Models (LLM), Retrieval-Augmented Generation (RAG), voice recognition, and multi-modal AI—through its flexible and advanced development capabilities.

3. Provision of High-Quality Data Sets

The company provides "rights-cleared" data sets that are essential for AI training, ensuring both quality and legal compliance for its clients.

■Strengths and Track Record

The company’s client base primarily consists of enterprise-level organizations. It supports numerous top players across various industries, notably including the provision of Japanese data for Meta’s Large Language Models (LLM). Rather than being just an AI tool provider, the company has garnered significant market attention as a key player that controls "data"—the very essence of AI.

-------------【 Job Description】-------------

■Recruitment Background]

The company provides a product platform for generating, preparing, and evaluating training data—an indispensable foundation for AI model development.

In recent years, particularly within the field of computer vision (CV):

・Models are advancing rapidly across object detection, segmentation, and tracking.

・Requirements for data volume and quality are rising sharply to support production deployment.

・Domain-specific data challenges are emerging in industrial sectors such as manufacturing, inspection, and blueprint analysis.

・Vision data processing and simulation (data augmentation) are accelerating in Physical AI and robotics.

As a result, the primary determinant of model performance has shifted from algorithm choice alone to "the ability to continuously design optimal training data."

Currently, the industry faces three critical challenges:

・Training data quality management remains heavily reliant on individual intuition.

・A disconnect exists between static evaluation metrics and real-world operational performance.

・Methodologies for data refinement have not yet been systematically established.

To address these challenges, the company requires engineers who can continuously elevate CV model performance through a data-centric lens.

The company is seeking a Research Engineer to take charge of data design and the evaluation feedback loop in the CV domain.

■Challenges in the Computer Vision Domain

Computer Vision is a field of technology that provides computers with human-like visual perception and recognition capabilities.

By extracting features from images and video feeds, it enables systems to:

・Identify what an object is

・Locate where it is positioned

・Understand how it moves

Key technical domains include:

・Image Classification: Technologies that identify target object categories across an entire image.

・Object Detection: Technologies that simultaneously pinpoint the locations and classifications of objects within an image.

・Segmentation: Advanced recognition technologies that classify and segment objects at the individual pixel level.

・Tracking: Technologies that continuously track object movement across video frames.

・Simulation: Data augmentation strategies leveraging tools such as NVIDIA Cosmos and Isaac Sim.

■Essence of the Role

The core objective of this position is to build data design frameworks and evaluation loops that ensure computer vision models train accurately, operate reliably in production environments, and effectively solve targeted challenges.

You will work not merely with clean image datasets, but with complex data presenting inherent CV challenges, including:

・Noisy real-world data

・Highly imbalanced class distributions

・Long-tail edge cases and anomalies

■Responsibilities

・Data design for object detection and segmentation models

・Preprocessing and quality control of image and video data

・Design of annotation policies and guidelines

・Design of model evaluation metrics and evaluation analysis

・Establishment of a feedback loop for training → evaluation → data improvement

・Performance improvement through collaboration with the model development team

■Research & Application Themes]

In this role, the engineer will engage with key focus areas such as:

Computer Vision Data Design

・Designing data distributions to maximize model performance

・Improving data efficiency in small-data environments

・Addressing long-tail class distribution challenges

Evaluation Design

・Establishing evaluation metrics that accurately reflect real-world operational performance

・Analyzing root causes of false positives and false negatives

・Developing methodologies to evaluate annotation quality

Industrial Applications

・Designing anomaly detection datasets for manufacturing lines

・Designing datasets for structural blueprint understanding and 3D reconstruction

・Adapting machine vision models to real-world environments

■Core Focus Areas & Key Expectations

・Data design and quality control for CV models

・Model evaluation and establishing a data improvement loop

・Image data processing and analysis using Python

・Improving model performance from a data perspective

■What You’ll Gain in This Role

・Hands-on experience in data design that influences CV model performance.

・Experience solving machine vision challenges in real-world environments.

・A career spanning both research and product development.

・Opportunities to contribute to the design of infrastructure that supports the societal implementation of AI.

-------------【 Requirements】-------------

Required

- Experience working on projects in the field of computer vision, specifically in data preprocessing, annotation design, or evaluation and analysis.

- Experience processing and analyzing image data using Python.

- Experience working to improve model performance from a data perspective.

- A basic understanding of computer vision technologies (such as object detection and segmentation).

- Understanding and knowledge of cameras and sensors.

Preferred

- Practical experience with object detection and segmentation models.

- Experience in dataset design and annotation quality control.

- Experience addressing the long-tail and data imbalance issues in image data.

- Experience applying computer vision in industrial fields (e.g., manufacturing, inspection, drawing analysis).

- Experience in research activities, implementing research papers, and presenting at academic conferences.

- Research achievements and experience in Physical AI (IL, VLA).

Ideal Applicants

- Finds genuine interest in elevating model performance through data architecture and evaluation loops, rather than focusing solely on model architectures in isolation.

- Driven to work on deployed, production-grade AI products rather than remaining confined to theoretical research.

- Eager to lead data design initiatives while maintaining continuous dialogue with model developers and AI engineers.

- Ready to take full ownership and responsibility as a critical backbone of commercial AI products.

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◾️Holidays & Leave

・Annual Paid Leave

・Saturdays, Sundays, and National Holidays

・Summer Vacation

・Year-End and New Year Holidays

・Special Leave for Family Events

・Maternity Leave

・Childcare Leave

・Menstrual Leave

◾️Employee Benefits

・Full Social Insurance Coverage

・Full transportation expense reimbursement

・Health checkups

・Influenza vaccinations

・Book purchase expense coverage

・Conference participation fee coverage

・In-house study sessions

・Company meal expense support

・1-on-1 meetings

・Free drinks

・Monitor loan

・Vending machines available

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