Generative AI Researcher

At a glance

$40–50/hr

Remote

Employer is flexible for the right candidate

Job

Full-time

US work authorization required

Role: Conducts fundamental research into new algorithms and techniques in generative AI.

Responsibilities:

Develop and test new generative models (e.g., GANs, VAEs, transformers).

Publish research papers and contribute to the academic community.

Investigate novel approaches to improve AI systems' performance, efficiency, and scalability.

Work on improving the understanding of model explainability, fairness, and ethical implications of AI.

2. Machine Learning Engineer

Role: Implements and optimizes machine learning models, including generative models, for practical applications.

Responsibilities:

Design, train, and deploy AI models.

Work with large datasets and use techniques such as transfer learning or data augmentation.

Implement tools for model monitoring, version control, and debugging.

Ensure models perform well in production environments, addressing issues related to scalability and latency.

3. Data Scientist

Role: Uses statistical analysis, machine learning, and data mining to extract insights and build predictive models.

Responsibilities:

Collect, clean, and preprocess large datasets to train generative models.

Analyze model outputs and iterate on model development.

Interpret results from the AI models to inform decision-making and improve business strategies.

Work with engineers to improve the training pipelines and data pipelines.

4. AI Product Manager

Role: Oversees the development of AI-powered products, ensuring they align with business needs and user requirements.

Responsibilities:

Define the product roadmap for AI-based features and products.

Gather requirements from stakeholders and translate them into technical specifications.

Prioritize product features based on user feedback, business goals, and technological constraints.

Collaborate with engineering and research teams to deliver AI solutions.

5. AI Ethicist

Role: Focuses on the ethical implications of AI, ensuring models and systems are aligned with ethical standards.

Responsibilities:

Identify potential ethical risks in AI models, such as bias, fairness, and accountability.

Develop guidelines for the responsible use of generative AI in different industries.

Work with legal and compliance teams to ensure AI applications comply with data privacy laws and other regulations.

Promote transparency and explainability in AI systems.

6. Generative AI Engineer/Developer

Role: Specializes in the development of generative AI applications.

Responsibilities:

Build and deploy applications using generative AI models (e.g., text generation, image generation, music composition).

Fine-tune pre-trained models and integrate them into existing applications.

Design the system architecture for scaling generative models in production.

Work on user interfaces and tools for non-technical users to interact with AI models.

7. AI Research Scientist (Specialized in Generative Models)

Role: Focuses specifically on advancing generative models, such as GPT, DALL-E, and other deep learning techniques.

Responsibilities:

Work on novel architectures for generative models (e.g., GANs, autoregressive models, VAEs).

Conduct experiments to improve model efficiency and creativity.

Develop training techniques for large-scale, high-quality generative models.

Contribute to open-source projects and academic publications.

8. AI Data Engineer

Role: Designs and manages the data infrastructure for AI systems, ensuring efficient collection, storage, and processing of data.

Responsibilities:

Build data pipelines to support training of generative AI models.

Optimize data storage and retrieval systems for large datasets (e.g., using cloud services like AWS, GCP, or Azure).

Ensure data quality, integrity, and consistency for training models.

Work on automation for model retraining using updated data.

9. AI Trainer/Annotator

Role: Labels and annotates data, and sometimes provides feedback for supervised fine-tuning of AI models.

Responsibilities:

Manually review, label, and annotate data for training purposes.

Provide feedback on model output to refine and improve the model's accuracy.

Work closely with data scientists and engineers to ensure high-quality data for training.

10. AI Quality Assurance (QA) Engineer

Role: Ensures the accuracy, reliability, and functionality of AI systems before they are deployed.

Responsibilities:

Develop testing strategies for AI models and ensure they perform as expected under different scenarios.

Test AI models for biases, fairness, and ethical issues.

Identify edge cases and test AI systems for robustness in real-world conditions.

Provide feedback to engineering teams for model improvement and bug fixing.

11. AI Architect

Role: Designs the overall structure and architecture of AI systems, ensuring scalability and efficiency.

Responsibilities:

Design and implement AI system architectures that support generative models.

Work on integrating AI systems with other enterprise applications.

Ensure that AI solutions are scalable and maintainable.

Lead teams in selecting the appropriate technologies and tools for AI model development.

12. AI Deployment/Operations Engineer

Role: Focuses on the deployment, monitoring, and maintenance of generative AI models in production.

Responsibilities:

Deploy models to production environments (cloud, on-premise).

Monitor the performance of generative AI models in real-time, addressing issues related to latency or errors.

Automate processes for continuous deployment and integration.

Ensure model scalability and optimize resource usage.

13. AI Business Analyst

Role: Works on aligning AI capabilities with business needs, analyzing the impact of AI on the business.

Responsibilities:

Analyze market trends and customer requirements to guide AI product development.

Quantify the ROI of AI solutions for business decision-makers.

Help with the commercial strategy and go-to-market plans for generative AI products.

Work with product teams to measure AI's impact on business outcomes and performance.