Job Description

As a Machine Learning Engineer, you will be responsible for developing, deploying, and monitoring advanced machine learning models to enhance our digital content authentication and deepfake detection solutions. You will support the entire model development lifecycle from prototyping to deployment, and engage closely with research scientists and software engineers to bring cutting-edge models into production.

Key Responsibilities:

• Model Development: Design and develop machine learning models based on project requirements and research findings.
• Model Training: Implement and optimize model training processes, ensuring high accuracy and performance.
• Evaluation: Rigorously evaluate models using relevant metrics and datasets to ensure robustness and reliability.
• Deployment: Deploy machine learning models to production environments, ensuring seamless integration with existing systems.
• Collaboration: Collaborate with research scientists and software engineers throughout the model lifecycle to ensure alignment and successful implementation.
• Research Reproduction: Reproduce new research and state-of-the-art methods to continually improve model performance and capabilities.
• Monitoring: Monitor models in production, analyze performance metrics, and incorporate customer feedback to refine and improve model outputs.

Qualifications:

• Education: Bachelor's or Master’s degree in Computer Science, or a related field.
• Experience: 2+ years of experience developing, training and deploying machine learning models in support of commercial software applications, preferably those focused on cybersecurity, computer vision or audio signal processing.
• Skills:
• Proficiency in programming languages such as Python and TypeScript.
• Strong knowledge of machine learning algorithms and principles.
• Expertise with machine learning frameworks such as PyTorch, Scikit-learn, TensorFlow, etc.
• Strong understanding of statistical analysis, data mining, and data processing techniques.
• Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud).
• Familiarity with deploying scalable models using Docker, Kubernetes and AWS SageMaker.
• Experience with data visualization tools and techniques.
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills.

Compensation and Benefits:

• Competitive salary and stock options
• Comprehensive health, dental, and vision insurance
• Generous paid time off and company holidays
• Professional development opportunities
• Hybrid work environment, with 3 days per week expected in office in Austin, TX

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