AI Data Trainer (The Model Mentor)

Are you passionate about working with data to train and improve AI models? Do you excel in creating datasets that empower machine learning models to perform with precision and accuracy? If you’re ready to shape the intelligence of AI systems, our client has the ideal role for you. We’re looking for an AI Data Trainer (aka The Model Mentor) to prepare, label, and refine data that directly impacts the performance and learning of AI algorithms across diverse applications.

As an AI Data Trainer at our client, you’ll collaborate with data scientists, machine learning engineers, and product teams to curate, clean, and label datasets. Your work will be instrumental in teaching AI models how to understand and respond to real-world scenarios, from image recognition to natural language understanding.

Key Responsibilities:

1. Curate and Label Training Data:
• Select, label, and organize datasets that enable machine learning models to learn effectively. You’ll ensure data is representative, balanced, and relevant for the models’ intended tasks.
2. Refine and Clean Datasets:
• Preprocess and clean raw data to eliminate noise and inaccuracies, ensuring high-quality data inputs. You’ll apply best practices to enhance data accuracy and reliability.
3. Collaborate on Annotation Guidelines:
• Work with data scientists and product managers to define labeling and annotation guidelines that align with project goals. You’ll standardize processes to maintain consistency across datasets.
4. Optimize Data for Model Performance:
• Analyze and select data subsets to improve model training efficiency and accuracy. You’ll monitor model performance to identify and address gaps in data quality or diversity.
5. Conduct Quality Assurance on Labeled Data:
• Perform quality checks on annotated data to ensure labels are accurate and useful for model training. You’ll provide feedback to improve data annotation workflows and tools.
6. Monitor and Report Data Training Results:
• Track the impact of training data on model performance, and create reports to communicate improvements and insights. You’ll work closely with machine learning engineers to refine datasets iteratively.
7. Stay Updated on AI Data Annotation and Training Trends:
• Keep up with advancements in AI training techniques, annotation tools, and data management. You’ll incorporate new tools and methods to streamline and improve data preparation.

Requirements

Required Skills:

• Data Annotation and Labeling: Extensive experience with data labeling tools and techniques, with the ability to create high-quality, structured datasets.
• Data Cleaning and Preprocessing: Strong understanding of data cleaning methods to prepare datasets for model training. You can handle structured and unstructured data formats.
• Analytical Skills and Quality Assurance: Ability to assess data quality and accuracy through quality assurance processes. You can troubleshoot and refine data to enhance model performance.
• Collaboration and Communication: Strong communication skills to work with cross-functional teams and explain data requirements. You’re detail-oriented and organized in managing datasets.
• Technical Skills: Proficiency in data manipulation using tools like Python or SQL. Familiarity with data labeling platforms (e.g., Labelbox, Amazon SageMaker Ground Truth, or Supervisely) is beneficial.

Educational Requirements:

• Bachelor’s or Master’s degree in Data Science, Computer Science, Information Management, or a related field. Equivalent experience in data annotation, AI training, or machine learning support may be considered.
• Certifications in data science or machine learning fundamentals are advantageous but not required.

Experience Requirements:

• 3+ years of experience in data preparation, annotation, or data quality management with hands-on experience training machine learning models.
• Familiarity with different types of data (text, images, audio) and experience in preparing data for supervised learning models.
• Experience in data labeling and curation for specific AI tasks, such as NLP or computer vision, is highly beneficial.

Benefits

• Health and Wellness: Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums.
• Paid Time Off: Competitive vacation, sick leave, and 20 paid holidays per year.
• Work-Life Balance: Flexible work schedules and telecommuting options.
• Professional Development: Opportunities for training, certification reimbursement, and career advancement programs.
• Wellness Programs: Access to wellness programs, including gym memberships, health screenings, and mental health resources.
• Life and Disability Insurance: Life insurance and short-term/long-term disability coverage.
• Employee Assistance Program (EAP): Confidential counseling and support services for personal and professional challenges.
• Tuition Reimbursement: Financial assistance for continuing education and professional development.
• Community Engagement: Opportunities to participate in community service and volunteer activities.
• Recognition Programs: Employee recognition programs to celebrate achievements and milestones.