Entry-Level Coding Jobs (Remote)

Frequently Asked Questions

• Q: What is an entry-level coding job in AI/ML?

  It is a junior role focused on data operations and simple engineering tasks that support AI/ML workflows. Typical duties include Python scripting, dataset preparation, prompt evaluation, RLHF data collection, named entity recognition, computer vision annotation, content safety labeling, and large language model evaluation.

• Q: Is this role fully remote?

  Yes. Positions on Rex.zone are designed for remote work with asynchronous collaboration. Teams may request overlapping hours for standups, code reviews, and evaluation cycles, but flexibility is standard.

• Q: Which skills help me get hired quickly?

  Basic proficiency in Python or JavaScript, Git, JSON/CSV handling, REST APIs, simple SQL, Linux shell, and careful attention to annotation guidelines. Clear communication and the ability to follow SOPs are essential.

• Q: What domains can I work in?

  Openings cover NLP, computer vision, content safety, LLM training and evaluation, RLHF pipelines, and general data labeling. Some roles emphasize QA evaluation, dataset curation, or prompt writing and testing.

• Q: What employment types are available?

  Remote contract, freelance, full-time, part-time, and internships. Entry-level and junior roles are most common, with senior opportunities for candidates who progress to lead evaluation, tooling, or data engineering responsibilities.

• Q: How do I apply via Rex.zone?

  Create a profile, upload your resume and portfolio links, and use search modifiers such as remote, contract, freelance, full-time, entry-level, or senior. Submit applications directly on Rex.zone and complete any short skills assessments.

• Q: What compensation should I expect?

  Pay varies by employer type, region, and task complexity. Entry-level rates are typically competitive for remote work, with increases tied to technical scope, quality metrics, and sustained performance.

• Q: Do I need prior experience?

  Formal experience is not required for many entry-level listings. Demonstrate potential with personal projects, bootcamp work, notebooks, clean code samples, and attention to training data quality and guideline adherence.

• Q: Which tools will I use day to day?

  VS Code, Jupyter, GitHub, issue trackers, Docker, annotation tools like Label Studio or CVAT, and cloud dashboards. Many teams use standardized QA and evaluation frameworks for LLMs and computer vision.

• Q: What does career progression look like?

  You can move from junior coder or annotator into QA lead, evaluation engineer, data engineer, or MLOps roles. Success is measured by reliable execution, annotation guidelines compliance, and contributions to model performance improvement.