Machine Learning Engineer

• Contribute to the full range of stages of AI/ML projects, from early explorations to productization and maintenance
• Work closely with data scientists and network security experts to design and implement scalable ML pipelines for large datasets
• Design and implement strategies to deploy LLMs and for fine-tuning
• Conduct experiments, analyze results and resource consumption using various metrics and visualization techniques
• Drive adoption of ML development best practices in code health, quality, test stability and maintainability
• Participate in technical discussions within the Labs team and collaborate with other teams across the organization
• Work in an Agile development team focused on exploring and delivering ML use cases

• Strong appreciation for our core values: low ego results, tireless service, and applied curiosity
• 3+ years experience developing, optimizing, troubleshooting, and maintaining large-scale machine learning systems
• Experience in DevOps and/or MLOps: continuous integration and delivery, automation of ML workflows, and deployments to production environments
• Understanding of data preprocessing, feature engineering and machine learning algorithms
• Understanding of model testing and validation techniques for ensuring model quality and performance
• Experience in Python and data science libraries (Pandas, Scikit-Learn, Keras, PyTorch, Tensorflow)
• Experience using Docker and/or Kubernetes, and containerized applications
• Excellent communication skills to work effectively in a team
• Degree in Computer Science or related fields, or equivalent experience

• Experience adopting and using Agile development tools and methodologies, and working in a distributed team
• Experience with local deployments and fine-tuning of LLMs
• Familiarity with the network security domain
• Experience using feature stores and ML frameworks like Kubeflow, Cortex, Seldon, or BentoML
• Experience in designing and implementing data pipelines using Apache Spark
• Experience with cloud computing, especially Databricks and AWS Services (EC2, S3, Cloudwatch)
• Experience with experiment tracking and reproducibility tools

Equity and additional benefits will also be awarded