Freelance Machine Learning Engineer

Remote from: Spain
Salary, yearly, USD: 68,000
Department: Software Engineering
Employment type: Full Time,
Job posted: 15 May 2026
Apply before: 15 Jun 2026
Experience level: Midweight
Views / Applies: 19 / 1

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

Actively Hiring

AI Summary

This freelance Machine Learning Engineer role with Mindrift involves designing computational STEM problems for AI evaluation. You'll create Python-based problems requiring non-trivial reasoning, verify solutions, and document them. The position requires 5+ years of ML experience, expert Python skills, and proficiency in SQL and GenAI technologies. It offers part-time, project-based work with an estimated 10-20 hours per week and up to $34 per hour.

Job Complexity

Easy Hard

AI Insight The role demands advanced technical skills including expert Python, ML algorithms, and GenAI, plus problem design and documentation. The need for 5+ years experience and proven business impact makes it challenging but not the hardest level.

Salary Analysis

AI Insight The offered salary of $68,000 per year (based on $34/hour for 10-20 hours/week) is below the typical US market range for Machine Learning Engineers, which is $90,000-$170,000. This is likely due to the freelance, part-time nature and project-based compensation.

Key Skills

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

Cover Letter Sample

I am excited to apply for the Freelance Machine Learning Engineer position at Mindrift. With over 5 years of hands-on experience in machine learning and a strong background in Python programming, I have successfully delivered impactful projects that demonstrate my ability to solve complex computational problems. My expertise in statistical analysis, SQL, and GenAI technologies aligns perfectly with the requirements of this role.

I am particularly drawn to the opportunity to design original STEM problems that push the boundaries of AI systems. My experience with frameworks like TensorFlow and PyTorch, combined with my proficiency in data science libraries, ensures I can create rigorous, computationally intensive problems. I am confident in my ability to verify solutions and document them clearly.

As a proactive and detail-oriented professional, I am comfortable working on a project basis and adapting to varying workloads. I look forward to contributing to Mindrift's mission of advancing AI through thoughtful evaluation. Thank you for considering my application.

Possible Interview Questions

Can you describe a complex computational problem you designed and how you ensured it could not be solved manually within a reasonable timeframe?

I designed a problem involving optimizing a supply chain network with stochastic demand, requiring a combination of linear programming and Monte Carlo simulation. The problem's size and randomness made manual solution impractical, and I verified the solution using Python with scipy.optimize and numpy.

How do you approach verifying a solution for a computationally intensive problem? Provide an example.

I break down the problem into smaller steps, write unit tests for each component, and compare results with known outputs for simpler cases. For example, in a problem involving matrix factorization, I validated the implementation against a small dataset where the correct factorization was known.

What experience do you have with GenAI technologies like LLMs and RAG? How have you applied them?

I have worked with LLMs for text generation and classification tasks, and implemented RAG pipelines using vector databases like Pinecone. For instance, I built a question-answering system that retrieved relevant documents from a knowledge base and used GPT-3 to generate answers.

Explain a time you had to optimize a Python solution for performance. What techniques did you use?

I optimized a data processing pipeline by using vectorized operations with numpy instead of loops, and parallelized tasks with multiprocessing. The runtime decreased from hours to minutes for a large dataset.

How do you ensure the problems you design are both challenging and solvable within the constraints of the project?

I start with a clear definition of the problem's scope and constraints, then design a solution algorithm that is non-trivial but feasible with standard libraries. I test the solution on smaller versions to estimate runtime and adjust complexity accordingly.

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 $34per 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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