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Mimecast
Mimecast
1,001 – 5,000 Employees
H1B Visa SponsorCybersecuritySecurity
Mimecast is a cybersecurity company that helps organizations address persistent digital threats through security services designed around privacy, resilience, and user experience. Its team of more than 2,500 employees works across international markets, including the United States, the United Kingdom, and Australia. The company emphasizes anticipating emerging risks, strengthening protection over time, and maintaining a culture focused on security excellence and continuous improvement.
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Machine Learning Engineer II — Hybrid in Columbus

Develop scalable machine learning, generative AI, and real-time data pipelines for Mimecast’s Incydr insider-risk cybersecurity product. Lead production deployments and cross-functional initiatives that advance customer-facing capabilities.

USUnited StatesMAMassachusettsMNMinnesotaOHOhio
$124,000 – $186,000 / year
HybridFull-timeMiddleSeniorEnglish RequiredMachine Learning Engineer
Amazon RedshiftAWSC++JavaKotlinPythonPyTorch

Description

  • Research, design, develop, and maintain advanced machine learning models
  • Train, evaluate, and fine-tune models for selection, validation, accuracy, latency, and throughput
  • Recommend approaches for scalability, model tuning, and data-infrastructure configuration
  • Design and implement complete data and machine learning pipelines for real-time products
  • Source, clean, and transform raw data through feature engineering
  • Prepare machine learning models for production and integrate them into existing systems
  • Monitor deployed models for effectiveness, throughput, and latency
  • Help shape software architecture for high-volume datasets
  • Lead machine learning projects from initial concept through production release
  • Mentor junior colleagues and promote engineering best practices
  • Partner with Product, Engineering, Marketing, Customer Success, Sales, and other product-development teams
  • Develop customer-facing features and predictive models through research and experimentation
  • Explain complex technical concepts in knowledge-sharing sessions
  • Manage, shape, and prioritize work with limited supervision
  • Build stakeholder relationships and support a collaborative, inclusive, continuously improving team culture
  • Use AI development tools to explore concepts, prototype solutions, interpret data, and experiment with LLMs and agent-based systems

Requirements

  • Ph.D. or master’s degree in a quantitative field plus at least four years applying advanced machine learning to industry problems, or a bachelor’s degree plus at least six years of directly relevant experience
  • Advanced programming skills in Python, C++, Java, or Kotlin
  • Practical experience with PyTorch, NLTK, spaCy, OpenCV, Tesseract, and Hugging Face
  • Understanding of linear algebra, stochastic optimization, and probability theory
  • Knowledge of statistical inference and machine learning methods, including forecasting, time-series analysis, hypothesis testing, anomaly detection, classification, and regression
  • Experience working with large-scale datasets containing more than two million training examples and highly imbalanced data
  • Proficiency with AWS services such as ECS, Kinesis, Lambda, S3, Glue, SageMaker, Bedrock, Athena, RDS, and Redshift
  • Experience designing and deploying scalable generative AI services with LangChain, LlamaIndex, n8n, and Spring AI
  • Ability to develop and maintain infrastructure as code
  • Experience using version control systems
  • Understanding of sensitive-data handling under master service agreements and compliance obligations
  • Strong analytical, problem-solving, communication, collaboration, adaptability, ownership, and attention-to-detail skills
  • Ability to explain technical concepts and business implications to non-technical audiences, plus successful completion of applicable background checks
  • Successful completion of applicable background checks

Benefits

  • Comprehensive benefits package
  • Structured and on-the-job learning opportunities
  • Hybrid work model with individual flexibility
  • Inclusive and diverse workplace
  • Interview adjustments or accommodations for disabilities or other needs
  • Incentive plans may be available
  • Additional benefits provided under company policy and applicable local regulations

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