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Joining Razer will place you on a global mission to revolutionize the way the world games. Razer is a place to do great work , offering you the opportunity to make an impact globally while working across a global team located across 5 continents. Razer is also a great place to work, providing you the unique, gamer-centric #LifeAtRazer experience that will put you in an accelerated growth, both personally and professionally. Job Responsibilities/ 工作职责 : Responsibilities: Collaborate with stakeholders to understand business objectives and define problems that can be addressed through machine learning and artificial intelligence. Collect, preprocess, and analyze large datasets to extract meaningful patterns and insights. Choose appropriate machine learning algorithms based on the nature of the problem, dataset characteristics, and desired outcomes. Develop and train machine learning models using programming languages like Python or R and frameworks such as TensorFlow or PyTorch. Identify and engineer relevant features from the data to enhance the predictive capabilities of machine learning models. Integrate machine learning models into existing systems or develop new applications that leverage machine learning capabilities. Optimize machine learning solutions for scalability and efficiency, particularly when dealing with large-scale datasets or real-time applications. Collaborate with cross-functional teams, including data scientists, software engineers, and business analysts. Stay informed about the latest advancements in machine learning, artificial intelligence, and related technologies. Address ethical considerations related to bias, fairness, and privacy in machine learning models. Maintain comprehensive documentation for machine learning models, including code, model architectures, and parameters. Implement security measures to protect machine learning models and data from potential vulnerabilities. Establish monitoring mechanisms to track the performance of deployed machine learning models over time. Requirements: Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related field. Proficiency in programming languages such as Python or R. Experience with machine learning frameworks like TensorFlow or PyTorch. Strong understanding of statistical analysis and data mining techniques. Ability to preprocess and analyze large datasets. Experience in developing and deploying machine learning models to production environment that serve millions of end users. Familiarity with software development practices and version control systems. Excellent problem-solving skills and attention to detail. Strong communication and teamwork abilities. Preferred Qualifications: Experience with deep learning architectures and algorithms. Knowledge of big data tools and platforms. Familiarity with cloud services related to machine learning. Publications or contributions to the machine learning community. Proven track record of implementing, maintaining and optimizing production machine learning models. 岗位职责： 与相关利益方合作，理解业务目标，并定义可通过机器学习和人工智能解决的问题。 收集、预处理和分析大规模数据集，以提取有意义的模式和见解。 根据问题类型、数据集特性和预期结果选择合适的机器学习算法。 使用Python 或 R等编程语言以及TensorFlow 或 PyTorch等框架开发和训练机器学习模型。 识别和工程化关键特征，以增强机器学习模型的预测能力。 将机器学习模型集成到现有系统，或开发新的应用程序以利用机器学习能力。 针对大规模数据集或实时应用场景，优化机器学习解决方案，提升可扩展性和计算效率。 与跨职能团队（如数据科学家、软件工程师、业务分析师）协作。 关注机器学习、人工智能及相关技术的最新进展。 解决机器学习模型中涉及的公平性、偏差和隐私保护等伦理问题。 维护机器学习模型的全面文档，包括代码、模型架构和参数。 实施安全措施，保护机器学习模型和数据免受潜在漏洞的影响。 建立监控机制，持续跟踪已部署机器学习模型的运行表现。 岗位要求： 计算机科学、数学、统计学或相关领域的学士或硕士学位。 熟练掌握Python 或 R等编程语言。 具备使用TensorFlow 或 PyTorch等机器学习框架的经验。 深入理解统计分析和数据挖掘技术。 能够预处理和分析大规模数据集。 具备开发和部署机器学习模型到生产环境的经验，能够支持百万级终端用户。 熟悉软件开发实践和版本控制系统。 具备优秀的问题解决能力和高度的细节关注度。 具备良好的沟通能力和团队协作能力。 优先条件： 具备深度学习架构和算法的经验。 了解大数据工具和平台。 熟悉云服务中的机器学习解决方案。 在机器学习领域有论文发表或社区贡献经验。 具备实施、维护和优化生产环境机器学习模型的成功案例。 Pre-Requisites/ 任职要求 : Are you game?
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.