ML Engineering Intern
@ Crossing Hurdles

Hybrid
$127,400
Hybrid
Contractor
Posted 19 hours ago

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Job Details

About ML Engineering Intern

Crossing Hurdles is a recruitment firm referring top candidates to partners working with world-leading AI research labs. The ML Engineering Intern role is a contractor position (hourly) that supports cutting-edge AI research efforts.

Role Responsibilities

  • Build and maintain GitHub-based project infrastructure including CI/CD workflows.
  • Set up and manage containerized environments using Docker and related tools.
  • Develop and integrate coding tool environments for CLI and API interactions.
  • Support reinforcement learning (RL) and large language model (LLM) research experiments and prototypes.
  • Handle data collection, preprocessing, and analysis for ML projects.
  • Collaborate asynchronously with research teams and document workflows and experimental outcomes.

Ideal Qualifications

  • Background in machine learning, reinforcement learning, or relevant coursework.
  • Experience with LLM or RL projects, contributions on GitHub.
  • Proficiency with Docker, CLI tooling, and GitHub project management.
  • Experience in building integrations and managing data pipelines.
  • Ability to balance engineering tasks with research activities.
  • Experience in research lab projects is a plus.

Application Process

Candidates should upload their resume, participate in an AI interview (15 minutes), and submit a form.

Key skills/competency

  • Machine Learning
  • Reinforcement Learning
  • GitHub
  • CI/CD
  • Docker
  • CLI
  • API
  • Data Preprocessing
  • Research
  • Project Management

How to Get Hired at Crossing Hurdles

🎯 Tips for Getting Hired

  • Research Crossing Hurdles' culture: Review firm mission, values, and news.
  • Customize your resume: Highlight ML, RL, and Docker skills.
  • Showcase GitHub projects: Emphasize CI/CD and tool integrations.
  • Prepare for the AI interview: Practice explaining project experiences clearly.

📝 Interview Preparation Advice

Technical Preparation

Review Docker and container management basics.
Practice setting up GitHub CI/CD workflows.
Study CLI tool integration techniques.
Refresh ML and RL research methods.

Behavioral Questions

Describe teamwork in remote projects.
Give an example of adapting to changes.
Explain handling ambiguous project directions.
Discuss managing multiple priorities.

Frequently Asked Questions