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. A day in the life - Spending time designing and implementing machine learning models, with a particular focus on NLP and reinforcement learning applications - Conducting data preprocessing tasks including cleaning datasets, normalizing values, and engineering features to prepare data for model training - Running model training sessions and fine-tuning parameters to optimize model performance - Working closely with software engineers to integrate machine learning models into Amazon's GenAI middleware systems - Testing and evaluating model performance using various metrics and cross-validation techniques - Implementing and monitoring reinforcement learning mechanisms to capture user feedback and improve system performance - Attending collaboration sessions with cross-functional team members including data scientists, software engineers, and UX/UI designers - Maintaining detailed documentation of model architectures, training procedures, and evaluation results About the team We are a team part of Enterprise Engineering at Amazon focused on revolutionizing how Amazonians work and collaborate through advanced generative AI technologies. Our mission is to transform productivity across the organization by developing AI solutions. We are highly motivated, collaborative, and maintain an entrepreneurial spirit with a bias for action. Our team brings together diverse talents including data scientists, machine learning engineers, software engineers, and UX/UI designers, all working in concert to create innovative AI solutions. We value technical excellence while maintaining a fun-loving atmosphere that encourages creativity and innovation. We operate at the forefront of AI technology, tackling complex challenges that require innovative thinking and technical expertise. Our team culture emphasizes continuous learning, invention at scale, and a deep commitment to delighting our customers through transformative solutions. If you're passionate about pushing the boundaries of what's possible with AI while working in a collaborative, fast-paced environment, you'll find yourself at home with our team.
