机器学习工程师（中级）
Machine Learning Engineer (Mid-Level)
这是一个远程职位。
职位描述：我们正在寻找一位技能娴熟且有动力的机器学习工程师加入我们的团队。在Ventera，作为机器学习工程师，你将有机会参与利用AWS机器学习生态系统的前沿项目（如SageMaker、EC2/ECS、S3存储桶等）。你的主要职责包括使用AWS服务开发、部署和维护数十个机器学习模型，并优化数据流水线以实现最大效率。目前模型开发的重点是异常检测时间序列预测，未来还将增加更多项目。
关键职责：
- 与跨职能团队合作，理解业务需求并开发机器学习解决方案。
- 使用AWS SageMaker、EC2/ECS、AWS SDK、S3存储桶及其他AWS ML生态系统工具构建、训练和部署大规模机器学习模型。
- 开发并维护干净、高效且文档齐全的Python代码，遵循最佳编码实践。
- 熟悉深度学习框架如PyTorch/Tensorflow以及其他机器学习包库，用于设计和实现机器学习算法（例如用于时间序列的DARTS，用于树状解决方案的PyCaret等）。
- 创建和分析数据集，进行实验，并调整模型以达到最佳性能。
- 使用SageMaker Notebook和其他相关工具进行数据探索、可视化和模型评估。
- 跟踪最新的机器学习和AWS服务进展，推动团队创新。
要求
资格：
- 计算机科学、机器学习或相关领域的学士或硕士学位。
- 2至3年机器学习工程师的实际工作经验。
- 精通Python，具备良好的编码能力，注重代码的清晰和高效。
- 具备AWS服务经验，特别是SageMaker、EC2/ECS、AWS SDK和S3存储桶。
- 熟悉AIML框架，如PyTorch/Tensorflow或其他开源库。
- 具备大型语言模型（LLM）微调和训练技术的知识或经验。
- 强大的分析和解决问题的能力。
- 优秀的沟通和团队协作能力。
- 有责任心、能完成任务的态度。虽然我们是远程办公，但这里的团队文化很好，我们希望保持这种优秀的团队！
加分项：
- 具备Docker和容器化方面的经验。
查看英文原文
This is a remote position.
Job Description: We are seeking a skilled and motivated Machine Learning Engineer to join our team. As a Machine Learning Engineer at Ventera, you will have the opportunity to work on cutting-edge projects that leverage the AWS Machine Learning ecosystem (SageMaker, EC2/ECS, S3 buckets, etc.). Your primary responsibilities will involve developing, deploying, and maintaining dozens of machine learning models in production using AWS services, as well as optimizing data pipelines for maximum efficiency. The current model development focus is on Anomaly Detection Timeseries prediction, with more added projects in the future. Key Responsibilities: - Collaborate with cross-functional teams to understand business needs and develop machine learning solutions. - Utilize AWS SageMaker, EC2/ECS, AWS SDK, S3 buckets and the rest of the AWS ML ecosystem to build, train and deploy machine learning models at scale. - Develop and maintain clean, efficient, and well-documented Python code following best coding practices. - Knowledge of deep learning frameworks such as PyTorch/Tensorflow, and other ML packaged libraries to design and implement machine learning algorithms (i.e. DARTS for timeseries, PyCaret for tree-based solutions, etc.). - Create and analyze datasets, conduct experiments, and fine-tune models to achieve optimal performance. - Use SageMaker Notebooks and other relevant tools for data exploration, visualization, and model evaluation. - Stay up to date with the latest advancements in machine learning and AWS services to drive innovation within the team.
Requirements
Qualifications: - Bachelors or Masters degree in Computer Science, Machine Learning, or a related field. - 2 to 3 years of hands-on experience as a Machine Learning Engineer. - Proficiency in Python and strong coding skills with a focus on clean and efficient code. - Experience with AWS services, particularly SageMaker, EC2/ECS, AWS SDK, and S3 buckets. - Familiarity with AIML frameworks such as PyTorch/Tensorflow/other open-sourced libraries. - Knowledge of/experience in LLM (Large Language Models) fine-tuning and training techniques. - Strong analytical and problem-solving skills. - Excellent communication and teamwork abilities. - Great all around get it done attitude. Although we work remotely, the team here has a great culture, and we are looking to maintain that great team! Additional Nice to Have Qualifications: - Previous experience with Docker and containerization within AWS. - Knowledge of serverless computing using AWS Lambda. - Understanding of MLOps and DevOps practices, particularly model deployment. - Experience with version control systems like Git. - Experience with multivariate time series forecasting. - Experience using publisher/subscriber for messaging queues. - Experience developing front end applications for data science POCs. - Experience in an Agile coding environment is a bonus (though not required, that can be picked up quickly).
Benefits
- Competitive salary
- Fully paid CareFirst BCBS Medical, Dental, and Vision coverage for you and your family
- Amazing team and great management that takes good care of their employees
- Generous paid time off and 11 paid holidays
- 401(k) retirement plan with employer matching
- Performance-based bonus system
- Professional development budget
Originally posted on Himalayas