Machine Learning Developer (Freelance)

Remote from: Germany
Salary, yearly, USD: 116,000
Department: Software Engineering
Employment type: Full Time,
Job posted: 25 May 2026
Apply before: 25 Jun 2026
Experience level: Midweight
Views / Applies: 21 / 6

Mindrift connects AI experts and clients to advance Generative AI models.

Actively Hiring

AI Summary

Mindrift is seeking a freelance Machine Learning Developer to design complex STEM problems and create Python-based solutions for AI training. The role involves developing computationally intensive problems requiring non-trivial reasoning and verifying solutions with standard libraries. Ideal candidates have 5+ years of ML experience, expert Python skills, and knowledge of GenAI and MLOps. This is a project-based, part-time opportunity with flexible hours (10-20 hours/week) and compensation up to $58/hour. The role is suitable for experienced ML specialists looking for non-permanent, high-impact projects.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight The role requires expert-level ML and Python skills, designing original problems, and solving computationally intensive tasks, which demands high expertise and creativity.

Salary Analysis

AI Insight The provided salary of $116,000 per year is competitive for a freelance ML developer, falling within the market range of $90,000-$160,000. The hourly equivalent of up to $58 is attractive for project-based work.

Key Skills

Machine Learning Python Data Science GenAI Problem Solving Statistical Analysis SQL MLOps PyTorch TensorFlow

Cover Letter Sample

Dear Hiring Team,

I am excited to apply for the Machine Learning Developer (Freelance) role at Mindrift. With over 5 years of hands-on experience in machine learning and a strong portfolio of projects, I am confident in my ability to design complex STEM problems and develop Python-based solutions that meet your requirements.

My expertise includes expert-level Python programming with libraries like Numpy, Pandas, and Scikit-learn, as well as deep knowledge of statistical analysis, SQL, and GenAI technologies. I have successfully created computationally intensive problems for AI training and am adept at verifying solutions efficiently.

I am particularly drawn to the flexibility and impact of project-based work, and I am eager to contribute to your AI improvement initiatives. Thank you for considering my application.

Sincerely,

Possible Interview Questions

Can you describe a complex STEM problem you designed that required a non-trivial reasoning chain to solve?

I designed a problem involving optimizing a multi-objective function under constraints using evolutionary algorithms, requiring the candidate to implement a custom genetic algorithm and compare it with gradient-based methods.

How do you ensure that a problem you create is computationally intensive and cannot be solved manually within reasonable timeframes?

I design problems with large combinatorial spaces or high-dimensional data, and include steps that require iterative computation or matrix operations that scale poorly with manual effort.

What is your experience with GenAI technologies, specifically LLMs and RAG?

I have worked on projects using LangChain to build RAG systems, integrating vector databases like Pinecone, and fine-tuning LLMs for domain-specific tasks. I understand prompt engineering and retrieval strategies.

Describe a time you used MLOps practices to deploy a machine learning model. What tools did you use?

I used Docker and Kubernetes to containerize and deploy a PyTorch model for real-time inference. I set up CI/CD pipelines with GitHub Actions and monitored model performance with MLflow.

How do you stay updated with the latest developments in machine learning and Python libraries?

I follow key conferences like NeurIPS and ICML, read papers on arXiv, and regularly experiment with new libraries. I also participate in online communities like Kaggle and GitHub to learn from others.

Please submit your CV in English and indicate your level of English proficiency.

Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.

What this opportunity involves

While each project involves unique tasks, contributors may:

Design original computational STEM problems that simulate real scientific workflows
Create problems that require Python programming to solve
Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks)
Develop problems requiring non-trivial reasoning chains and creative problem-solving approaches
Verify solutions using Python with standard libraries (Numpy, Pandas, Scipy, scikit-learn)
Document problem statements clearly and provide verified correct answers

What we look for
This opportunity is a good fit for ML specialists with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:

5+ years of hands-on machine learning experience with proven business impact
Portfolio of completed projects and publications showcasing real-world problem-solving
Expert Python programming for data science (pandas, numpy, scipy, scikit-learn, statsmodels)
Expert statistical analysis and machine learning – deep understanding of algorithms, methods, and their practical applications
Expert with SQL and database operations for data manipulation and analysis
Experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases)
Understanding of MLOps practices and model deployment workflows
Knowledge of modern frameworks (TensorFlow, PyTorch, LangChain)
Strong written English (C1+).

How it works

Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid

Project time expectations

For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.

Compensation

On this project, contributors can earn up to $58 per hour equivalent, depending on their level and pace of contribution.

Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

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