Machine Learning Engineer
2 weeks ago
Role: ML Engineer
- The Machine Learning Engineer will be responsible for designing new AI models and developing the new system. This role will encompass a variety of tasks, with one of the core duties being to collaborate closely with product owners and business partners to develop AI models capable of addressing complex business challenges. Given the critical importance of vulnerability prioritization for information systems security, the new custom-built tool will cover as much of the legacy system's functionality as possible based on Sony's requirements. This new tool will also incorporate additional flexibility for custom calculations and reports.
- This is an individual contributor role with a deep passion for the field of cybersecurity. The role is 100% focused on new projects and enhancements.
Must-haves :
- Proficiency in designing, developing, and researching Machine Learning systems, models, and schemes.
- Ability to study, transform, and convert data science prototypes into practical applications.
- Experience in searching and selecting appropriate datasets for data collection and modeling.
- Strong background in performing statistical analysis and using results to enhance ML models.
- Skilled in training and retraining ML systems and models as necessary.
- Ability to identify differences in data distribution that could impact model performance in real-world scenarios.
- Proficient in visualizing data to extract deeper insights.
- Familiarity with analyzing use cases of ML algorithms and ranking them by their success probability.
- Understanding when findings can be applied to business decisions.
- Experience in enriching existing ML frameworks and libraries.
- Knowledge of verifying data quality and ensuring it through data cleaning processes.
- Excellent problem-solving skills, creativity, and the ability to adapt to new attack surfaces.
Preferred :
- 5+ years experience as a Machine Learning Engineer or a similar role.
- Advanced skills in programming languages such as Python, Java, or C++.
- Experience with machine learning frameworks such as PyTorch and libraries (e.g., NumPy, Pandas, or Keras).
- Adept at using big data processing tools (e.g., Hadoop, Spark) and data storage systems (e.g., SQL, NoSQL).
- Strong knowledge of the foundational concepts of MLOps, application development, infrastructure management, data engineering, and data governance.
- Prior experience of training, retraining, deploying, scheduling, monitoring, and improving models, as well as designing and creating scalable, performant solutions.
- Preferred experience with big data, statistics, and Machine Learning.
- Preferred experience with algorithms and optimization.
- Excellent problem-solving and analytical skills.
- Effective communication and collaboration abilities.
- Ability to work independently when needed.
- Knowledge of core security controls and systems such as risk analysis quantification and points of escalation.
- Knowledge of IT security regulations and standards, such as ISO.
- Strong analytical and critical thinking skills.
- Understanding of Agile processes and principles.
- Preferred experience with Azure DevOps.
Day-to-day Responsibilities :
- Collaborate with cross-functional teams to develop and implement machine learning models and algorithms.
- Analyze large, complex datasets to extract actionable insights and improve machine learning model performance.
- Develop and maintain machine learning pipelines, including data preprocessing, feature extraction, model training, and evaluation.
- Deploy machine learning models into production environments and monitor their performance.
- Stay current with industry advancements in machine learning and artificial intelligence and apply relevant techniques and technologies to improve existing models and systems.
- Contribute to the development of best practices, guidelines, and standards for machine learning engineering within the organization.
- Collaborate on IT projects to ensure that risk issues and security policy are addressed throughout the project lifecycle.
- Adhere to the Scaled Agile Framework methodologies and tools that exist in the environment.
- Participate in daily stand-up of Compliance & Security release train and contribute to bi-weekly sprints.
- Maintain User Stories and Tasks for Operational & Engineering activities.
Additional Responsibilities :
- 3+ years experience designing, building and deploying machine learning applications in one of the domains of text understanding, entity recognition, NLP experience, computer vision, recommendation systems, or search.
- 1+ years of experience with writing stable and production level pipelines for model training and evaluation leading to reproducible models and metrics.
- Experience with data analytics and wielding SQL+pandas+spark framework to both build data and metric generation pipelines and answer critical questions about system efficacy or counterfactual treatments.
- Ability to understand business requirements thoroughly and bias toward designing a simplest yet generalizable ML model / system that can accomplish the goal.
- Uses a systematic approach to debug both data and system issues within ML / heuristics models.
- Fluent with Python and machine learning toolkits like numpy, sklearn, pytorch and tensorflow.
- Effective software engineering skills who can find answers quickly from code base and writes structured, readable, well tested and efficient code.
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