How often have you had an opportunity to be an early member of a team that is tasked with solving a huge customer need through disruptive, innovative technology, reinventing an industry? Do you apply Machine Learning to big data problems? Are you excited by analyzing and modeling terabytes of data that solve real world problems? We love data and have lots of it. We’re looking for an engineer capable of using machine learning and statistical techniques to create solutions for non-trivial, and arguably, unsolved problems.
We are working on revolutionizing the way Amazonians work and collaborate. Our team is on a mission to transform productivity through the power of advanced generative AI technologies. In pursuit of this mission we are seeking a motivated Machine Learning Engineer to join our team. The successful candidate will be responsible for developing, implementing, and optimizing machine learning models that will drive our generative AI initiative. This role involves close collaboration with data scientists, software engineers, and UX/UI designers to create a seamless and context-aware AI solution that enhances productivity across various user personas within Amazon.
You will join a highly motivated, collaborative and fun-loving team with an entrepreneurial spirit and bias for action. The role will challenge you to think differently, hone your skills, and invent at scale. We're looking for engineers who obsess over technical details but can delight customers by continually learning and building the right products. You will help to invent the future of advertising.
Technical Skills needed:-
- Programming Languages: Proficiency in Python, including libraries such as TensorFlow, PyTorch, and scikit-learn.
- Experience with R or Java is a plus.
- Machine Learning and AI: Strong understanding of machine learning algorithms and frameworks. - Experience with natural language processing (NLP) techniques and models.
- Familiarity with reinforcement learning and its applications.
- Knowledge of supervised and unsupervised learning methods.
- Data Preprocessing and Analysis: Expertise in data cleaning, normalization, and transformation. Ability to perform feature engineering and selection. Proficiency in data analysis tools and techniques.
- Model Development and Evaluation: Experience in developing, training, and fine-tuning machine learning models. Knowledge of model evaluation metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Familiarity with cross-validation techniques.
- Big Data Technologies: Experience with big data tools and frameworks like Hadoop, Spark, or Kafka. Proficiency in handling large datasets and optimizing data pipelines.
- API and Microservices Development: Experience in developing and deploying RESTful APIs. Familiarity with microservices architecture and related technologies.
- Cloud Platforms: Experience with cloud platforms such as AWS. Proficiency in using cloud-based machine learning and data storage services.
- Security and Privacy: Understanding of data privacy regulations and best practices. Experience with data anonymization techniques and secure data handling.
Key job responsibilities
1. Model Development: Design, develop, and implement machine learning models, particularly focusing on natural language processing (NLP) and reinforcement learning techniques.
2. Data Preprocessing: Perform data cleaning, normalization, and feature engineering to prepare datasets for model training.
3. Model Training: Train and fine-tune machine learning models to achieve high accuracy and robustness.
4. Integration: Work with the software engineering team to integrate ML models into the middleware that interfaces with Amazon’s GenAI offerings.
5. Performance Evaluation: Use cross-validation and various performance metrics (e.g., precision, recall, F1-score) to evaluate model performance and ensure their reliability.
6. Continuous Improvement: Implement reinforcement learning strategies to ensure the AI system continuously learns and improves from user interactions.
7. Collaboration: Collaborate with data scientists, software engineers, and UX/UI designers to ensure the models meet user requirements and integrate seamlessly with existing tools.
8. Documentation: Document model architectures, training processes, and evaluation results to ensure transparency and reproducibility.
We are open to hiring candidates to work out of one of the following locations:
Seattle, WA, USA
We are working on revolutionizing the way Amazonians work and collaborate. Our team is on a mission to transform productivity through the power of advanced generative AI technologies. In pursuit of this mission we are seeking a motivated Machine Learning Engineer to join our team. The successful candidate will be responsible for developing, implementing, and optimizing machine learning models that will drive our generative AI initiative. This role involves close collaboration with data scientists, software engineers, and UX/UI designers to create a seamless and context-aware AI solution that enhances productivity across various user personas within Amazon.
You will join a highly motivated, collaborative and fun-loving team with an entrepreneurial spirit and bias for action. The role will challenge you to think differently, hone your skills, and invent at scale. We're looking for engineers who obsess over technical details but can delight customers by continually learning and building the right products. You will help to invent the future of advertising.
Technical Skills needed:-
- Programming Languages: Proficiency in Python, including libraries such as TensorFlow, PyTorch, and scikit-learn.
- Experience with R or Java is a plus.
- Machine Learning and AI: Strong understanding of machine learning algorithms and frameworks. - Experience with natural language processing (NLP) techniques and models.
- Familiarity with reinforcement learning and its applications.
- Knowledge of supervised and unsupervised learning methods.
- Data Preprocessing and Analysis: Expertise in data cleaning, normalization, and transformation. Ability to perform feature engineering and selection. Proficiency in data analysis tools and techniques.
- Model Development and Evaluation: Experience in developing, training, and fine-tuning machine learning models. Knowledge of model evaluation metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Familiarity with cross-validation techniques.
- Big Data Technologies: Experience with big data tools and frameworks like Hadoop, Spark, or Kafka. Proficiency in handling large datasets and optimizing data pipelines.
- API and Microservices Development: Experience in developing and deploying RESTful APIs. Familiarity with microservices architecture and related technologies.
- Cloud Platforms: Experience with cloud platforms such as AWS. Proficiency in using cloud-based machine learning and data storage services.
- Security and Privacy: Understanding of data privacy regulations and best practices. Experience with data anonymization techniques and secure data handling.
Key job responsibilities
1. Model Development: Design, develop, and implement machine learning models, particularly focusing on natural language processing (NLP) and reinforcement learning techniques.
2. Data Preprocessing: Perform data cleaning, normalization, and feature engineering to prepare datasets for model training.
3. Model Training: Train and fine-tune machine learning models to achieve high accuracy and robustness.
4. Integration: Work with the software engineering team to integrate ML models into the middleware that interfaces with Amazon’s GenAI offerings.
5. Performance Evaluation: Use cross-validation and various performance metrics (e.g., precision, recall, F1-score) to evaluate model performance and ensure their reliability.
6. Continuous Improvement: Implement reinforcement learning strategies to ensure the AI system continuously learns and improves from user interactions.
7. Collaboration: Collaborate with data scientists, software engineers, and UX/UI designers to ensure the models meet user requirements and integrate seamlessly with existing tools.
8. Documentation: Document model architectures, training processes, and evaluation results to ensure transparency and reproducibility.
We are open to hiring candidates to work out of one of the following locations:
Seattle, WA, USA
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