Mimecast is seeking a Machine Learning Engineer II to join their Incydr product development team to solve challenges related to insider risk management by developing, deploying, and monitoring machine learning models.
Requirements
- Demonstrate advanced programming proficiency in Python, C++, Java, or Kotlin, and possess hands-on expertise with leading tools and frameworks including PyTorch, NLTK, Spacy, OpenCV, Tesseract, and Huggingface.
- Exhibit a robust understanding of linear algebra, stochastic optimization, and probability theory, with a proven ability to translate these concepts into practical real-world solutions.
- Possess deep theoretical and practical knowledge of statistical inference and machine learning, with experience in forecasting, time series analysis, hypothesis testing, anomaly detection, classification, and regression.
- Have a track record of working with large-scale datasets (exceeding two million training examples) and effectively managing highly imbalanced data.
- Be proficient in utilizing AWS data services such as ECS, Kinesis, Lambda, S3, Glue, Sagemaker, Bedrock, Athena, RDS, and Redshift for scalable data processing and deployment.
- Demonstrate experience in architecting and deploying scalable generative AI services leveraging frameworks such as Langchain, Llamaindex, n8n, and sprintAI
- Show capability in developing and maintaining infrastructure-as-code to automate and streamline the deployment of team-owned services.
Responsibilities
- Research, design, develop, and maintain state-of-the-art machine learning models optimized for accuracy, latency, and throughput.
- Train, evaluate, and fine-tune models using best practices in model selection, validation, and performance optimization.
- Provide recommendations and strategies to manage scalability, tuning, and other configurations within the data infrastructure.
- Design and implement robust, end-to-end data and ML pipelines capable of feeding real-time data products.
- Source, clean, and perform feature engineering on raw data using a variety of data tools and frameworks.
- Productionize and deploy ML models, ensuring seamless integration with existing systems.
- Monitor deployed models for efficacy, throughput, and latency, and iterate as needed to maintain optimal performance.
Other
- Embrace the role of "full-stack" data scientist, which will often require you to acquire and clean raw data, engage internal and external partners to obtain labels for that data, develop machine learning and statistical models, deploy models to production, and monitor the models for performance.
- Understand the business context and challenges as well as articulate practical solutions.
- Collaborate with other development teams and cross functional teams to provide features that bring value to our customers and help them secure their collaboration culture.
- Mentor and guide junior team members, establish and champion best practices, and foster a culture of continuous learning and improvement.
- Own, shape, and prioritize your work with minimal oversight, demonstrating strong self-management and accountability.
