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We are the Happy Elements AI team. We believe that the game industry is ripe for a disruption by artificial intelligence, because of the large untapped potential in big data generated by games.
Our team combines talents in machine learning, data science and engineering from both Silicon Valley and Beijing. Driven by our passion for artificial intelligence, we focus on generating the best gaming experience for players through AI technology.

MISSION STATEMENT

Generate the best gaming experience through AI

WHAT WE DO

Transform high volumes of gameplay data into actionable insights.
Research and develop machine learning systems for games.
Engineer infrastructure for experimentation, data science, and large-scale deployment.

Dr. Richard Chen

Richard Chen leads Happy Elements’ San Francisco Office and Happy Elements’ AI Team. Prior to Happy Elements, Richard conducted research in deep reinforcement learning at OpenAI. Previously, Richard worked on machine learning for mobile video advertising at Vungle Inc and quantitative investment strategies in Goldman Sachs. Richard received his PhD in Applied Math from California Institute of Technology and B.Sc. from Tsinghua University. 
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Dr. Rein Houthooft

Rein Houthooft leads Happy Elements AI Team. Originally from Belgium (EU), Rein received his PhD in EECS from Ghent University. Part of his research was conducted as a researcher at OpenAI and at the Berkeley AI Research lab of UC Berkeley, with a focus deep reinforcement learning and generative models. Furthermore, Rein is involved in the organization of the annual NeurIPS Deep RL Workshop.
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RECRUITMENT

Beijing

Software Engineer

Responsibilities

  1. Build report interfaces and data feeds
  2. Build server applications and online A/B testing system
  3. Implement web interfaces

Qualifications

  1. BS or MS in Computer Science or related field
  2. Expert in Java, knowledge in Python (become master in Python within 3 months)
  3. Knowledge of relational databases and SQL
  4. Knowledge with machine learning packages such as Scikit-learn and Tensorflow is a plus.
Data Engineer

Responsibilities

  1. Manage data warehouse
  2. Design, build and launch new data pipelines processes in production (extraction, transformation and loading)
  3. Work closely with machine learning engineers and provide data support for machine learning models in production
  4. Work with data infrastructure to triage infra issues and drive to resolution

Qualifications

  1. BS or MS degree in computer science or a related field
  2. Expert in SQL (Spark, Hive, etc)  and real time data processing
  3. Expert with dimensional data modeling and schema design in data warehouses
  4. Expert in custom or structured ETL design, implementation and maintenance
  5. Master in analyzing data to identify deliverables, gaps and inconsistencies
  6. Knowledge in Python
  7. Communication skills including the ability to identify and communicate data driven insights
Machine Learning Engineer

Responsibilities

  1. Build, maintain, and improve efficient and reliable data mining and machine learning models.
  2. Work closely with researchers in designing and implementing and tuning machine learning models, and provide performance feedback of machine learning models.
  3. Work closely with data engineers to adapt and improve data pipelines for production models.
  4. Work closely with software engineers in putting models into production (interface, SLA, scalability)

Qualifications

  1. Ms in Computer Science, System Engineering or related field with 2+ years of industry experience or PhD with 1+ years of industry experience.
  2. Expert in Python, and computation graph toolkits (e.g., Scikit-learn, Tensorflow). Solid experience with Python packages such as Numpy.
Data Analyst

Responsibilities

  1. Understand business objectives. Conduct analysis and build models for user behavior, in-game economics, and other topics.
  2. Work close with Data Scientists, Machine Learning Engineers and conduct analysis for Data Science and Machine Learning projects
  3. Generate productionable SQL queries and collaborate with data engineers for deploying SQL queries for regular analytic reporting.
  4. Work closely with data engineers, product teams and have thorough understanding of the company data and the data quality

Qualifications

  1. BS/MS in Mathematics, Statistics, Computer Science, with 3 years of working experience in data and analytic-intensive roles
  2. Expert in SQL or other relational database.
    • Capable of producing high-quality SQL queries that are modularized and readable.

    • Understand how to optimize SQL queries for lowering memory and compute resources.

  3. Solid understanding and experience in ETL-related systems/processes. They include Azkaban, Airflow, and other ETL management tools.

  4. Experience in Python and relevant Python packages such as numpy, scipy, panda, etc.

  5. Strong communication skills to present the result of analyses

Data Scientist

Responsibilities

  1. Understand business objectives and identify opportunities to apply Data Science techniques to achieve business impact
  2. Model and prototype business problems with quantitative methods including statistics, machine learning, etc, with the goal of production deployment
  3. Collaborate with product teams in gathering relevant product information.
  4. Work closely with data engineers to adapt and impr.
  5. ove data pipelines for Data Science modelingWork closely with machine learning researchers in designing and implementing machine learning models

Qualifications

  1. Ms in Mathematics, Statistics, Computer Science, or Economics
  2. Master in statistics and machine learning methods
  3. Expert in Python. Solid experience with Python packages such as Numpy, Panda, and Scikit-learn.
  4. Expert in SQL or other relational database.
  5. Strong communication skills to present the result of analyses
  6. Knowledge and experience in cloud computing is a plus

San Francisco

Machine Learning Engineer

Responsibilities

  1. Build, maintain, and improve efficient and reliable data mining and machine learning models.
  2. Work closely with researchers in designing and implementing and tuning machine learning models, and provide performance feedback of machine learning models.
  3. Work closely with data engineers to adapt and improve data pipelines for production models.
  4. Work closely with software engineers in putting models into production (interface, SLA, scalability)

Qualifications

  1. Ms in Computer Science, System Engineering or related field with 2+ years of industry experience or PhD with 1+ years of industry experience.
  2. Expert in Python, and computation graph toolkits (e.g., Scikit-learn, Tensorflow). Solid experience with Python packages such as Numpy.