Computer Scientist - AI Training (Freelance, Remote)

Overview

You will work on a variety of projects that involve labeling data, writing code, and providing feedback to improve the performance of AI models. Your contributions will directly impact the development of cutting-edge AI technology.

Responsibilities

• Data Labeling: Annotate and label datasets for various AI tasks such as natural language processing, computer vision, and more.

• Code Evaluation & Debugging: Analyze and assess code snippets generated by AI models, identifying errors and providing feedback for improvement.

• Model Training: Contribute to the training of AI models by providing high-quality labeled data and feedback on model performance.

• Collaboration: Work independently and as part of a team with other AI professionals, data scientists, and engineers.

Requirements

Experience Levels:

• Level 1 (Bachelor's Level):
  - Strong understanding of fundamental programming concepts.
  - Proficient in at least one programming language (e.g., Python, Java, C++).
  - Familiarity with data structures and algorithms.
  - Basic understanding of machine learning concepts is a plus.

• Level 2 (Master's Level):
  - In addition to Level 1 requirements, experience with software development methodologies (e.g., Agile).
  - Working knowledge of multiple programming languages.
  - Experience with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
  - Experience building and deploying machine learning models.

• Level 3 (PhD Level):
  - Expertise in advanced machine learning techniques (e.g., deep learning, reinforcement learning, NLP).
  - Strong research background in AI/ML, evidenced by publications or projects.
  - Ability to design and implement novel algorithms and models.
  - Excellent communication skills for explaining complex technical concepts.

Compensation

Pay Range (rate per hour): $15—$150 USD

Additional Information

Work Model:
Excel in a remote-friendly hybrid model. We embrace remote work but have transitioned to a hybrid model focusing on collaboration and connection within our tech hubs in the San Francisco Bay Area, New York City Metro Area, and Wrocław, Poland. We encourage asynchronous communication, autonomy, and ownership of tasks with the added convenience of hub-based gatherings.
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