Preferred Networks (PFN) is looking for highly motivated interns to work with us this summer. We welcome applications from students who are interested in working together to create new technologies, software, and services, not only in machine learning, but also in a wide range of other areas of computer science.

Index

• Features and Duration of a PFN Internship
• PFN Intern Online Information Session
• Theme List
• Key Qualifications
• Important Notes before You Apply
• How to Apply
• Selection Process
• Salary and Benefits

PFN Internship Overview

• Over the 1.5 month period, PFN researchers and engineers will be assigned to work with you as your mentors. You’ll have opportunities to discuss your internship themes and engage in research and development with the mentors specialized in various fields including deep learning, computer vision, robotics, life sciences, UI, and distributed processing.
• We encourage the results of the internship to be published in the forms of OSS, papers, and PFN Research & Development blogs if there are no confidentiality or rights issues.  
• PFN also values ​​the diversity of its members and the working environment. We are looking forward to receiving applications from women, internationals and students from all kinds of backgrounds. All applicants will be selected fairly based on their skills.

• Start date： Wednesday, August 3, 2022
• End date： Friday, September 16, 2022
  • Start and end dates are negotiable according to class schedules and other circumstances of the participants.
  • This year’s internship will end on September 16 since many schools start their fall semester in late September. 
  • If the schedule does not pertain to you and you need more time to finalize your research or need to spend more time with us, you can extend your internship until September 30 under the same terms and conditions. 
  • During your internship, we understand you may have to be absent for lab activities, academic conferences, and returning home for family commitments, etc. We are flexible about your need to take days off due to these reasons.

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Mentoring & Events in the office:

Last year, our internship was held fully remotely in response to the COVID-19 situation but this year we plan to provide some mentoring and events onsite at the PFN office with certain infection prevention measures. Although subject to change depending on the infection situation, we plan to have the interns come to the office about 2 or 3 days per week for in-person collaborations and presentations in addition to online mentoring. This is to make sure the interns can deepen their knowledge of our company through ample opportunities for communication, and make the internship fruitful.

Messages from our employees and the past interns:

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PFN Internship Online Information Session

The PFN Internship online information session will be held in Japanese. For details please check the Japanese page.

Theme List

• For the 2022 Domestic Internship at PFN, we have prepared two types of programs; research internship that aims to publish research papers, and project internship in which interns take part in ongoing projects at PFN. The themes for each program are listed below.
• During or after the selection process, you will make discussions with PFN members to finalize your theme. You can choose up to 2 themes in the application form. 
• Any intellectual property achieved through your internship activities shall belong to PFN. Therefore, when determining your internship theme, please ensure that you do not select the research theme you have worked on in your university research lab or other organization you belong to.

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Project Internships

[+] Computational Infrastructure

JP01. MN-Core向けのコンパイラ及び周辺ライブラリの開発

MN-Coreのコンパイラ及び周辺ライブラリの開発を行います。具体的には以下のようなテーマを想定しています
・ドメイン特化言語である JAX, Halide などの活用
・低精度での学習モデルの研究開発
・FFTなどMN-Core向けカーネルの実装
・プロファイラなどの周辺ツールの開発

Communication Language: Japanese

Must Requirements

・Python, C++を用いたコーディング能力

Want Requirements

・低レベル最適化の経験
・深層学習向けコンパイラの知識

JE01. Development of framework and library for deploying deep learning models in real world

(1) Develop compiler/runtime (PFVM) that optimizes computational graphs of deep learning models to perform inference performantly (in terms of execution time and memory usage), targetting on various backends such as CUDA or edge devices.
(2) Develop an open-source library that uses GPU (CuPy).

Communication Language: English/Japanese

Must Requirements

・Programming in C++
・Programming in Python
・Basic knowledge of computer science

Want Requirements

・Experience on deep learning model development
・Experience of running deep learning models on edge devices or smartphones
・Development experience of multi-pass compilers

JP02. 次世代MN-Coreのマイクロアーキテクチャ検討

次世代MN-Coreのマイクロアーキテクチャの、性能/電力/面積改善のための各種検討に取り組みます。

Communication Language: Japanese

Must Requirements

・コンピュータ・アーキテクチャの知識
・高いVerilog HDL/System Verilogコーディング技術
・論理シミュレーションによるRTLの検証経験

Want Requirements

・合成/配置配線ツールの使用経験
・STAの知識
・深層学習の基礎的な知識

JP03. エッジとクラウドの両方を考えた実用的なネットワークおよびシステムの設計と実装

IoTデバイスとクラウドを接続し、コストやスケーラビリティなども意識して実用的なシステムを構築します。安定性やメンテナンスの容易性などを考慮したネットワーク設計およびシステム設計とその実装を素早く行う必要がある課題に取り組みます。

Communication Language: Japanese

Must Requirements

・Pythonを用いたコーディング能力
・Web・ネットワークに関する基礎知識

Want Requirements

・AWSかGCPの知識、または経験

JP04. Kubernetesにおけるコンテナ実行環境の改善

PFNで運用中のKubernetesクラスタ (MN-2, MN-3) のコンテナ実行環境を改善するため、必要な周辺技術の調査、評価、システム設計の提案、開発などに取り組みます。コンテナ起動時間の高速化、Kubernetes上からFUSE(Filesystem in Userspace)を安全に使える方法の検討、コンテナネットワーキングスタックのリプレースといったテーマのどれかに取り組みます。

Communication Language: Japanese

Must Requirements

・コンテナ技術についての基礎知識、使用経験 (Dockerなど)
・Kubernetesの基礎知識

Want Requirements

・Goによるプログラミング経験
・コンテナランタイムの実装や関連仕様に関する知識
・コンピュータネットワークに関する基本的な知識（TCP/IP, Ethernetなど）
・コンテナネットワーキングの関連技術に関する知識や経験（CNI Plugin, BGP, eBPFなど）

JE02. Development and optimization of storage systems for deep learning and simulation

Our goal is to develop the technology to deliver the training data in storage media (either HDD or SSD), to the memory of GPU or MN-Core, as well as the technology to store the data obtained by simulation into our storage media.

Communication Language: English/Japanese

Must Requirements

・System programming in Linux (TCP/IP, Ethernet, system calls, file I/O, FUSE)

Want Requirements

・Kernel programming in Linux (VFS, kernel modules, etc.)

JP05. 大規模な機械学習向け計算基盤（インフラ技術）の研究開発

GPUやMN-Coreを用いた大規模計算機（クラスタ）の研究開発に取り組みます。計算基盤の性能改善、要素技術検証、より良い運用技術（可視化や自動化）などのテーマなどから取り組む内容を調整する予定です。

Communication Language: Japanese

Must Requirements

・自発的・意欲的に研究課題に取り組める能力

Want Requirements

・基本的なコンピューターサイエンスの知識やハードウェア技術

[+] System Development

JE03. Development and operation of a web system for Matlantis which is a general-purpose atomic-level simulator

Work on the development and operation of Matlantis features. (e.g., optimize GPU utilization, enhance system monitoring, cloud cost management, multi-region support, etc.)

Communication Language: English/Japanese

Must Requirements

・Familiarity with Python or Go

Want Requirements

・Experience with cloud computing (AWS/GCP/Azure, etc.)
・Experience with tools that enable Infrastructure as Code (Terraform / CloudFormation)
・Knowledge of container orchestration systems (Kubernetes)
・Client and server model implementation (REST/gRPC/GraphQL, etc.)

[+] Optimization

JE04. Optuna development

For Optuna, an OSS hyperparameter optimization framework, (1) develop new optimization algorithms, (2) improve the code quality and design, or (3) improve processes of OSS operation/support.

Communication Language: English/Japanese

Must Requirements

・Basic knowledge in computer science

Want Requirements

・Experience in the development of or contribution to open source softwares
・Experience of the development or support of software in a multi-person team
・Any achievement in machine-learning area such as paper pulication or awards at competitions
・Expert knowledge in Bayesian optimization, neural network search or AutoML

[+] Computer Vision

JP06. 機械学習・微分可能レンダラを用いた3次元復元

PFNで開発している3Dスキャナ (https://projects.preferred.jp/3d-scanner/) や関連技術の改善に取り組みます。例えば、以下の項目の改善を目指します。
・透明や複雑な構造の物体の3次元復元
・マテリアルの推定
・human-in-the-loopによる3次元復元や最適化
・3Dモデルの軽量化
・3次元復元の高速化

Communication Language: Japanese

Must Requirements

・Pythonを用いたコーディング能力
・深層学習フレームワークの使用経験（PyTorchなど）
・computer vision、3Dに関する知識

Want Requirements

・3次元復元、novel view synthesis、CGに関する知識や研究開発経験
・3DCG制作ソフトの使用経験

JP07. コンピュータービジョンのためのウェブアプリケーション開発

コンピュータビジョンに関わるツール（動画・3Dデータなどへのアノテーションツール、機械学習を活用したアノテーションツール、データセットやモデルの管理・可視化ツールなど）の開発に取り組みます。

Communication Language: Japanese

Must Requirements

・TypeScript, Reactを用いたウェブフロントエンドの開発能力

Want Requirements

・Go, Pythonを用いたコーディング能力

[+] Engineering Applications

JP08. 機械学習を用いた地震波解析

地下探査手法の一つである地震探査に関して、機械学習を用いた地震波解析に関わる技術開発に取り組みます。観測データとシミュレーションのギャップを埋め、地震波解析に適した深層学習モデル・手法の開発を目指します。

Communication Language: Japanese

Must Requirements

・計算機科学の基本的知識
・機械学習に関する応用的知識
・Pythonを用いたコーディング能力

Want Requirements

・深層学習フレームワークの利用経験（PyTorchを推奨）

JP09. 機械学習による時系列予測モデルの高度化

機械学習を用いた時系列予測モデルの精度向上・高速化・物理的制約の追加などの高度化に取り組みます。単なる予測精度向上に留まらず、実問題特有の物理制約や応用先の特徴を考慮した評価・改善を目指します。

Communication Language: Japanese

Must Requirements

・基本的なコンピュータサイエンスに関する知識
・機械学習に関する応用的知識
・Pythonを用いたコーディング能力

Want Requirements

・深層学習フレームワークの利用経験（PyTorchを推奨）

JP10. 機械学習による化学工学シミュレーションの高度化

化学工学分野の熱力学や化学反応に関するシミュレーションに対して機械学習を応用し、シミュレーションの高速化や、より現実に合うシミュレーションなどを目指していきます。

Communication Language: Japanese

Must Requirements

・基本的なコンピュータサイエンスに関する知識
・機械学習に関する応用的知識
・Pythonを用いたコーディング能力

Want Requirements

・化学工学、熱力学、データ同化、制御理論などの知識
・並列処理、CUDAのスキル・経験
・Rustの利用経験

[+] Drug Discovery・Materials Informatics

JE05. Development of library and/or user interface to accelerate drug discovery research

Will work on developement of tool(s) for excecution of machine learning models, management and visualization of the results to aid molecular design tasks for drug discovery. No specific knowledge of drug discovery or chemistry is assumed.

Communication Language: English/Japanese

Must Requirements

・Basic knowledge of computer science
・Knowledge of organic chemistry, medicinal chemistry or structural biology

Want Requirements

・Experience in team develoment
・Experience in development of user interface

JE06. Development of algorithms to calculate physical properties for Matlantis

Develop algorithms to calculate physical properties for Matlantis. This may involve implementing methods based on computational chemistry or developing models to predict physical property values from datasets.

Communication Language: English/Japanese

Must Requirements

・Coding skills in Python

Want Requirements

・Knowledge of computational chemistry
・Experience in using deep learning frameworks such as PyTorch

[+] Retail

JP11. 小売店向け画像認識システムに関連するソフトウェア・アプリケーション開発

小売店向け画像認識システムに関連した開発を行います。画像認識アルゴリズムに限らず、UIやバックエンド関連の開発も想定しています。

Communication Language: Japanese

Must Requirements

・Pythonを用いたコーディング能力

Want Requirements

・画像認識に関連する研究・開発の経験
・Webアプリケーションの開発経験

JP12. 小売業における業務課題解決・最適化

小売業における業務課題を発見し、POS・店舗内画像データ等を複合的に活用しながら、小売業務の再構築・業務品質の向上に資する手法を開発します。

Communication Language: Japanese

Must Requirements

・Pythonを用いたコーディング能力
・自発的かつ積極的に小売業界の知識をキャッチアップする意欲・能力

Want Requirements

・機械学習・深層学習フレームワークの利用経験（PyTorchを推奨）
・数理最適化に関する知識

[+] Autonomous Driving

JP13. 自動運転プランナ用の評価シミュレーション開発

自動運転プランナの評価に用いるシミュレータの開発に取り組みます。
(1) シミュレータで用いる他車挙動アルゴリズムの開発
(2) 効率的なプランナ検証シナリオ作成手法の開発
(3) 様々な評価指標の実装
(4) 大規模なシミュレーション環境構築

Communication Language: Japanese

Must Requirements

・C++ のコーディング能力
・基本的なコンピュータサイエンスに関する知識

Want Requirements

・自動運転プランニング技術に関する研究開発経験
・深層学習フレームワークの使用経験

JP14. 自動運転に用いる自己位置推定手法の開発

自動運転に用いる自己位置推定手法の開発に取り組みます。
(1) HD mapおよびカメラを用いた自己位置推定手法の開発
(2) 点群地図およびLiDAR点群データを用いた自己位置推定手法の開発

Communication Language: Japanese

Must Requirements

・C++ のコーディング能力
・基本的なコンピュータサイエンスに関する知識

Want Requirements

・自己位置推定手法に関する研究開発経験
・環境認識手法に関する研究開発経験

JP15. 自動運転に用いる物体認識手法の開発

自動運転に用いる認識アルゴリズムの開発に取り組みます。
(1) LiDAR、画像を用いた2次元・3次元物体認識手法の開発および改善
(2) センサーデータのノイズ除去アルゴリズムの開発

Communication Language: Japanese

Must Requirements

・基本的なコンピュータサイエンスに関する知識
・画像認識または点群処理の知識
・Python・C++を用いたコーディング能力

Want Requirements

・深層学習フレームワークの使用経験（PyTorch, TensorFlow等）
・2次元・3次元物体認識に取り組んだ経験・実績 (論文・学会発表があればより望ましい)

[+] Education

JP16. 教育のための学習管理システムの開発

教育サービス向けの学習管理システムの機能提案と開発を行います。

Communication Language: Japanese

Must Requirements

・Pythonを用いたコーディングスキル
・SQLや統計ソフトを利用したデータ処理の経験

Want Requirements

・Webフレームワークを用いた開発
・AWS、GCP等のクラウド利用経験
・教育への興味・情熱
・仮説を立て、自力で提案から開発までを行う遂行能力

[+] Entertainment

JE07. Development of creative tools

In this project, you will be working with Entertainment team members at PFN. You will build creative tools that empowers human creativity with technologies.

Communication Language: English/Japanese

Must Requirements

・Basic knowledge in computer science
・Communication skills in either Japanese or English

Want Requirements

・Experience in using deep learning frameworks such as PyTorch
・Skills in application and web development
・Like anime/game

Research Internships

[+] Machine Learning in General

JE08. Resolving the gap between training and test environments

Collecting big data for machine learning is hard; in many real-world situations, it might cost a fortune or it might be physically impossible. This is why the world today is striving to develop methods that can train a model efficiently from small datasets, or a way to use a simulator to create a good artificial dataset. Either way, however, we must overcome the gap between the training dataset and the test dataset collected from the real environment. We are looking for diligent, excited applicants who will join us in our endeavor to develop a method for training a model that can generalize on the test environment.

Communication Language: English/Japanese

Must Requirements

・Record of publication in top-tier conference/journal as a corresponding author
・Deep knowledge of Machine Learning

Want Requirements

・Ph.D. candidacy
・Experiences with Deep learning frameworks (preferably PyTorch)
・Capability of propelling your R&D projects proactively and in a self-motivated manner: identify problems, analyze causes, devise solutions

JE09. Deep learning for tabular data with missing values

In general, tabular data has various type of attributes taking continuous values or discrete values. For some cases, the attribute values are randomly or structully missing. Here we will consider a deep learning for such tabular data.

Communication Language: English/Japanese

Must Requirements

・Familiarity with the Python programming
・Experience in Deep Learning framework such as PyTorch
・Good understanding of machine learning

Want Requirements

・Capability of propelling your R&D projects proactively and in a self-motivated manner: identify problems, analyze causes, devise solutions
・Experiences in writing a technical paper

[+] Computer Vision

JP17. 機械学習・微分可能レンダラを用いた3次元復元

人物や動体や高難度の物体の3次元復元のタスクのための研究開発に取り組みます。例えば、以下の項目に取り組みます。
・NeRFなどのニューラルネットを活用した3D表現や、微分可能可能レンダリングのアルゴリズムの研究開発
・髪の毛などの精細な箇所の復元品質の向上
・表現形式や圧縮の工夫による動体復元データの軽量化
・時間変化のあるシーン、人物、物体の3次元復元の高速化
・透明や複雑な構造の物体の3次元復元やマテリアルの推定

Communication Language: Japanese

Must Requirements

・Pythonを用いたコーディング能力
・深層学習フレームワークの使用経験（PyTorchなど）
・computer vision、3Dに関する知識

Want Requirements

・関連国際会議や論文誌への投稿経験
・3次元復元、novel view synthesis、CGに関する知識や研究開発経験
・3DCG制作ソフトの使用経験

JE10. Research on machine learning methods for medical imaging

We will develop machine learning methods to solve problems in the medical imaging domain (e.g. domain adaptation, medical image synthesis, registration, etc.).

Communication Language: English/Japanese

Must Requirements

・Deep knowledge of image analysis methods
・Coding skills in Python
・Experience using deep learning frameworks such as PyTorch

Want Requirements

・Knowledge of biology equivalent to at least undergraduate level
・Experience of research with images or video data
・Publishing papers in the field of biology, mathematics, or informatics
・Experience developing applications using machine learning or deep learning

JE11. Image processing and superresolution for remote sensing data

Although synthetic-aperture radar (SAR) imaging has an advantage in imaging of the surface ground irrespective of weather or time, it suffers low SN ratio. You will work on this SAR image to extract useful information by region detection or superresolution.

Communication Language: English/Japanese

Must Requirements

・Expertise in Deep Learning-based machine learning
・Good experiences in developing machine learning models in Pytorch
・Capability of propelling your R&D projects proactively and in a self-motivated manner: identify problems, analyze causes, devise solutions

Want Requirements

・Experiences in writing a technical paper
・Knowledgable about, and/or having R&D experiences on satellite image processing
・Eager to start R&D activities from collecting and organizing your own dataset

JE12. Depth estimation from video

Training of depth esitimation model from video generated by simulated and real images.

Communication Language: English/Japanese

Must Requirements

・Coding skills in Python
・Experience using deep learning frameworks such as PyTorch
・Good knowledge of computer vision / image analysis methods

Want Requirements

・Capability of propelling your R&D projects proactively and in a self-motivated manner: identify problems, analyze causes, devise solutions
・Experiences in writing a technical paper

[+] Engineering Applications

JE13. Machine learning of weather forecasting model based on atmospheric physics

You will research on the machine learning of weather forecasting model which is based on atmospheric physics.

Communication Language: English/Japanese

Must Requirements

・Expertise in Deep Learning-based machine learning
・Good experiences in developing machine learning models in Pytorch
・Capability of propelling your R&D projects proactively and in a self-motivated manner: identify problems, analyze causes, devise solutions

Want Requirements

・Experiences in writing a technical paper
・Knowledge of atmospheric physics

JE14. Sensory data compression with deep learning

Progresses in remote sensors yield a tremendous amount of sensory data available at sensor sites. To make then valuable, we need to distribute the large sensory data to several user sites in time efficiently. To this end, you will research and develop a DNN model for the lossy compression of these sensory data, which are quite different from ordinary images and texts.

Communication Language: English/Japanese

Must Requirements

・Expertise in Deep Learning-based machine learning
・Good experiences in developing machine learning models in Pytorch
・Capability of propelling your R&D projects proactively and in a self-motivated manner: identify problems, analyze causes, devise solutions

Want Requirements

・Experiences in writing a technical paper
・Knowledgable about, and/or having R&D experiences on lossy compression on media data such as images, movies, audios
・Knowledgable about, and/or having R&D experiences on image denoising
・Knowledgable about, and/or having R&D experiences on satellite image processing
・R&D experiences in a multi-person team with Git, Github
・R&D experiences with virtual container/environment such as Docker, poetry, virtualenv
・Having experiences on computing experiments on GPU clusters (private or public)
・Accustomed to command-line interfaces in Linux and/or Mac
・Eager to start R&D activities from collecting and organizing your own dataset

[+] HCI

JE15. Interactive system for HCI for ML

Develop interactive systems to help building machine learning models using human knowledge and experience. More specifically, we will conduct research on a framework to assist users in solving image recognition tasks with small amounts of training data.

Communication Language: English/Japanese

Must Requirements

・Familiarity with the Python programming
・Experience in user interface development (any platform)

Want Requirements

・Experiences in writing a technical paper
・Experience in deep learning frameworks (e.g., PyTorch)

[+] Communication

JE16. Just-in-time data communication protocol development in a heterogeneous network environment

The Internet has become heterogeneous as various data links such as low-latency and high-capacity optical fiber networks, 4G/5G cellular data networks with limited bandwidth, low-orbit satelite and high-latency geostationary-orbit satelite links. Internet applications have also diversified. The requirements to the network vary by applications. For example, interactive applications such as online meeting tools and online games require low latency, and software update or content delivery require effiicient data delivery over high bandwidth networks. In this research theme, we develop a data commuinication protocol that satisfy “just-in-time” requirements of various applications and achieve effficient network resource utilization.

Communication Language: English/Japanese

Must Requirements

・Capability of propelling your R&D projects proactively and in self-motivated manner: identify problems, analyze causes, devise solutions
・Strong interests in network and low-layer programming
・Experitse on transport layer protocols such as TCP and the Internet protocol

Want Requirements

・Knowledge on congestion control technologies
・Practical programming experience

JE17. Efficient data transfer technology for large-scale deep learning

Large-scale deep learning relies on huge amount of data. To accelerate deep learning training, it is required to efficiently deliver such huge amount of data from a sensor site. In case computing resources such as GPU and deep learning accelerators spread to multiple data centers, it is also required to transfer huge amount of training data and trained models between these data centers. In this reseach theme, we will investigate state-of-the-art data center network technologies, inter-data center network and storage management technologies, and develop an effective and efficient data transfer method from sensor sites to data centers and between multiple data centers.

Communication Language: English/Japanese

Must Requirements

・Capability of propelling your R&D projects proactively and in self-motivated manner: identify problems, analyze causes, devise solutions

Want Requirements

・Knowledge on computer network and datacenter network technologies

[+] Drug Discovery・Materials Informatics

JE18. Applied research on machine learning/molecular simulation for drug discovery

Will work on researches on molecular design for drug discovery. The project topic will be selected from 1) molecular generative model, 2) molecular property prediction, or 3) binding affinity evaluation by molecular simulation, and so on.

Communication Language: English/Japanese

Must Requirements

・Basic knowledge of computer science
・Knowledge of organic chemistry, medicinal chemistry or structural biology

Want Requirements

・Experience in team develoment
・Experience in generative models or molecular property/affinity prediction tasks (Publications or oral presentation in scientific conferences are preferred)

JE19. Applied research and development of machine learning and atomic simulation on materials

Will work on the development of machine learning based atomic simulator, or the application of machine learning to molecular dynamics calculations and materials exploration techniques. (Reference: Matlantis https://matlantis.com/)

Communication Language: English/Japanese

Must Requirements

・Basic knowledge of computer science
・Basic knowledge or interest of physic, chemistry, or material science

Want Requirements

・Knowledge of college degree in physics, chemistry, or materials science
・Computational science experience. For example, in-depth knowledge of and experience implementing and using simulation techniques such as quantum chemical calculations, molecular dynamics methods, etc.
・Experience in research and development and writing papers on the integration of physical simulation and machine learning

[+] Entertainment

JE20. Application of deep learning techniques to creation

In this project, you will be working with Entertainment team members at PFN. You will take part in the research of deep learning techniques for creativity.

Communication Language: English/Japanese

Must Requirements

・Familiarity with Python
・Communication skills in either Japanese or English

Want Requirements

・Experience in using deep learning frameworks such as PyTorch
・Publications in machine learning domain
・Domain knowledge in Computer Vision, Computer Graphics, Generative Model
・Skills in software development
・Like anime/game

[+] Finance

JE21. Deep learning for financial applications

PFN Quantitative Finance Team invites research internship applications on the topic of deep learning techniques for financial applications, including (1) Financial Time-Series Prediction, (2) Deep Hedging, and (3) Artificial Market Simulation. Our goal is to develop methods that allows efficient learning from small data enviromnents by leveraging financial domain knowledge/inductive biases.

Communication Language: English/Japanese

Must Requirements

・Coding skills in Python
・Experience in deep learning frameworks (e.g., PyTorch)

Want Requirements

・Experience in writing academic research papers
・Experience and/or knowledge in finance-related research fields

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Internship Location

PFN Tokyo Office
Otemachi Bldg., 1-6-1, Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004

Key Qualifications

• PFN is seeking highly motivated and skilled individuals who can develop applications, tools, etc., independently. 
• Knowledge and development experience based on the themes listed in [Theme List] will be considered in the selection process. Please check each theme for details. The common requirements are:
  • Currently enrolled in high school, technical college, university, or graduate school. Negotiable for those attending other higher education institutions
  • Fluent in Japanese or English
  • Strong communication skills
  • Must be able to come to our Tokyo office and work online on weekdays during the internship period (lodging assistance will be provided for those living far away).
  • Must have legal qualifications that allow internship / part-time job activities in Japan during the internship period (e.g. Japanese nationality, permanent residency, student visa.)

• Please do not hesitate to apply even if you don’t have prior experience in full-scale development. Re-entry for those who applied in the past is also possible (except for those who have already participated in past internship programs).

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Important Notes before You Apply

• Non-Japanese students who are studying at Japanese universities with a student visa must make sure to get【Permission for Other Activity / 資格外活動許可】before the start date of your PFN internship program.
• You need to let us know in advance if any administrative work is required to receive academic credit from your school. Please note that depending on the complexity of the work, PFN may not be able to accommodate your request.

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How to Apply

• Click this Application Form to apply. 
  • * You need to log in with your Google account to access the application form.
  • * Personal information entered in the application form will be used for the purpose of intern selection.

[Application deadline]

• April 26, 2022, 23:59 PM (Japan time, No application accepted after the deadline)

[Note on Application]

• Resume and file format are PDF only. Please submit using the application form.
• Everyone is requested to use the e-mail address used during the application process as the selection ID, so please do not use anything other than the e-mail address you registered with during the selection period.

[Notes on resume]

• Be as specific as possible in your resume so that we have all the information we need to be able to evaluate your abilities and experience.
• We will use the email address you used when you applied as your selection ID, so please make sure to use the same email when you submit your assignment or contact us for any reasons. 
•  The evaluators will read your resume and use it to determine whether it matches the theme you are applying for. Please be specific about your abilities and experiences related to the theme.
• Please describe your award history and research experience as comprehensively as possible. Even if you find your prior work unremarkable, it may be valuable for the evaluator. 

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Selection Process

Assignment selection (coding test & thematic assignment) → Online interview → Final result announcement

• Sunday 23:45 PM, May 8th, 2022:  Deadline for submission of assignments
• Monday, May 23rd, 2022: Announcement of the results of the assignment selection and guidance for the online interview
• Friday, July 1st, 2022: Final result announcement

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Salary and Benefits

Salaries

• 2,500 yen an hour for technical college, university, graduate school students
• 2,000 yen an hour for high school students
• Work hours：8 hours in principle. 5 days a week excluding Saturdays, Sundays, and public holidays.  

Others

• Earphone allowance: 3,000 JPY
• Commuting fee support：PFN will pay for your daily commute to and from the office in an approved route.
• Travel cost：For students traveling a long distance by plane or Shinkansen to participate in the internship, PFN will support the cost for one round trip to relocate between the Tokyo area and the place where you’re currently living.
• Accommodation support：For students coming from distant areas, PFN will provide an accommodation allowance of 5,000 yen per day for the entire period of your internship including holidays.
• You need to arrange a place to stay by yourself. Reasonable weekly rental apartments are available near the PFN office ranging from 100,000 to 150,000 yen a month.
• Please note that the accommodation allowance is taxable.

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