AI Strategy Consultant
@ Scale AI

San Francisco Bay Area
$50,000
On Site
Part Time
Posted 5 hours ago

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Job Details

About the Role

As an AI Strategy Consultant at Scale AI, you will advance cutting-edge AI innovations by conducting high-impact experiments and ensuring quality execution. You will work on research experiments, dataset generation, data quality improvements, and technical analysis.

Responsibilities

  • Design and execute research experiments
  • Build and evaluate frontier LLM datasets
  • Develop training and testing material for pipelines
  • Improve quality of existing and new products

Ideal Candidate

The ideal candidate is analytical, detail-oriented, and results-driven with strong problem-solving and communication skills. You must thrive in a fast-paced, entrepreneurial environment and be proactive in overcoming challenges.

Required Qualifications

  • Strong machine learning knowledge (ML PhD final years or graduate)
  • Excellent written and verbal communication skills
  • Action-oriented with creative problem solving
  • Experience in a fast-paced, entrepreneurial setting
  • Technical skills: Python, GPU, AWS, API, LLM, ML, SQL

Additional Information

This is a fully remote, US-based part-time contract (10-20 hours/week) positioned via HireArt. Scale AI values diversity and is an Equal Opportunity Employer. Reasonable accommodations are available upon request. Please note a 90-day waiting period applies for reconsideration for the same role.

Key skills/competency

  • AI
  • Research
  • Dataset
  • Analysis
  • ML
  • Python
  • AWS
  • Communication
  • Experimentation
  • Quality

How to Get Hired at Scale AI

🎯 Tips for Getting Hired

  • Research Scale AI's culture: Study mission, values, and projects.
  • Customize your resume: Highlight technical and analytical skills.
  • Showcase portfolio: Include relevant AI experiments and datasets.
  • Prepare for interviews: Focus on technical and problem-solving questions.

📝 Interview Preparation Advice

Technical Preparation

Review Python, AWS and ML libraries.
Practice dataset generation and quality testing.
Study LLM and API integration techniques.
Run mock experiments and data analysis exercises.

Behavioral Questions

Explain a time you solved a complex challenge.
Describe teamwork in a fast-paced setup.
Discuss handling multiple priorities effectively.
Share an experience of proactive problem solving.

Frequently Asked Questions